{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ecdfcf8e",
   "metadata": {},
   "source": [
    "# 案例数据背景介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3dfb5df",
   "metadata": {},
   "source": [
    "**1.数据来源：**\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d9aba3b0",
   "metadata": {},
   "source": [
    "Dongjin Cho，韩国蔚山国立科技学院城市与环境工程学院\n",
    "Cheolhee Yoo，韩国蔚山国立科技学院城市与环境工程学院"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6d6e21a",
   "metadata": {},
   "source": [
    "**2.摘要：**"
   ]
  },
  {
   "cell_type": "raw",
   "id": "02143fd2",
   "metadata": {},
   "source": [
    "本案例所使用的数据集包含了韩国首尔2013至2017年夏天的14个天气预报（NWP）的气象预报数据、2个现场观测数据和5个地理辅助变量。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d1dfde",
   "metadata": {},
   "source": [
    "**3.数据集信息**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f64e0e9",
   "metadata": {},
   "source": [
    "这些数据是为了修正韩国气象局在首尔运行的LDAPS(Local Data Assimilation and Prediction System)模式下次日最高和最低气温预报的偏差。该数据包括2013年至2017年的夏季数据。输入数据主要由LDAPS模型的次日预报数据、当前的现场最高和最低气温以及地理辅助变量组成。此数据有两个输出数据（即第二天的最高和最低气温）；2015年至2017年期间进行了后期验证。\n",
    "关于LDAPS模式: http://qikan.camscma.cn/jamsweb/cn/article/doi/10.11898/1001-7313.20200103?viewType=HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f5ecbf",
   "metadata": {},
   "source": [
    "**4.数据属性介绍**"
   ]
  },
  {
   "cell_type": "raw",
   "id": "81f19bea",
   "metadata": {},
   "source": [
    "1. station - 气象站编号: 1 - 25 \n",
    "2. Date - 记录日期: yyyy-mm-dd ('2013-06-30' to '2017-08-30') \n",
    "3. Present_Tmax - 记录日期的0-21时最高气温(Â°C): 20 - 37.6 \n",
    "4. Present_Tmin - 记录日期的0-21时最低气温(Â°C): 11.3 - 29.9 \n",
    "5. LDAPS_RHmin - LDAPSM模式预测的次日最小相对湿度 (%): 19.8 - 98.5 \n",
    "6. LDAPS_RHmax - LDAPS模式预测的次日最大相对湿度 (%): 58.9 - 100 \n",
    "7. LDAPS_Tmax_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 17.6 - 38.5 \n",
    "8. LDAPS_Tmin_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 14.3 - 29.6 \n",
    "9. LDAPS_WS - LDAPS 模式预测的次日平均风速 (m/s): 2.9 to 21.9 \n",
    "10. LDAPS_LH - LDAPS模式预测的次日平均潜热通量 (W/m2): -13.6 - 213.4 \n",
    "11. LDAPS_CC1 - LDAPS模式预测的次日第一个6小时平均云量 (0-5 h) (%): 0 - 0.97 \n",
    "12. LDAPS_CC2 - LDAPS模式预测的次日第二个6小时平均云量 (0-5 h) (6-11 h) (%): 0 to 0.97 \n",
    "13. LDAPS_CC3 - LDAPS模式预测的次日第三个6小时平均云量 (0-5 h) (12-17 h) (%): 0 to 0.98 \n",
    "14. LDAPS_CC4 - LDAPS模式预测的次日第四个6小时平均云量 (0-5 h) (18-23 h) (%): 0 to 0.97 \n",
    "15. LDAPS_PPT1 - LDAPS模式预测的次日第一个6小时平均降水量 (0-5 h) (%): 0 to 23.7 \n",
    "16. LDAPS_PPT2 - LDAPS模式预测的次日第二个6小时平均降水量  (6-11 h) (%): 0 to 21.6 \n",
    "17. LDAPS_PPT3 - LDAPS模式预测的次日第三个6小时平均降水量 (12-17 h) (%): 0 to 15.8 \n",
    "18. LDAPS_PPT4 - LDAPS模式预测的次日第四个6小时平均降水量  (18-23 h) (%): 0 to 16.7 \n",
    "19. lat - 维度 (Â°): 37.456 - 37.645 \n",
    "20. lon - 经度 (Â°): 126.826 - 127.135 \n",
    "21. DEM - 高程 (m): 12.4 - 212.3 \n",
    "22. Slope - 坡度 (Â°): 0.1 - 5.2 \n",
    "23. Solar radiation - 太阳辐射量 (wh/m2): 4329.5 to 5992.9 \n",
    "24. Next_Tmax - 次日最高气温 (Â°C): 17.4 to 38.9 \n",
    "25. Next_Tmin - 次日最低气温 (Â°C): 11.3 to 29.8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad62a46",
   "metadata": {},
   "source": [
    "# 数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 717,
   "id": "ee1ada92",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_profiling as pp\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Simhei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "#全部行都能输出\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "id": "65382592",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>Present_Tmin</th>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>...</th>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "      <th>Next_Tmin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>28.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>58.255688</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>23.006936</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.9</td>\n",
       "      <td>21.6</td>\n",
       "      <td>52.263397</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>24.035009</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>48.690479</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>24.565633</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>58.239788</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>23.326177</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.4</td>\n",
       "      <td>21.9</td>\n",
       "      <td>56.174095</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>23.486480</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   station       Date  Present_Tmax  Present_Tmin  LDAPS_RHmin  LDAPS_RHmax  \\\n",
       "0      1.0  6/30/2013          28.7          21.4    58.255688    91.116364   \n",
       "1      2.0  6/30/2013          31.9          21.6    52.263397    90.604721   \n",
       "2      3.0  6/30/2013          31.6          23.3    48.690479    83.973587   \n",
       "3      4.0  6/30/2013          32.0          23.4    58.239788    96.483688   \n",
       "4      5.0  6/30/2013          31.4          21.9    56.174095    90.155128   \n",
       "\n",
       "   LDAPS_Tmax_lapse  LDAPS_Tmin_lapse  LDAPS_WS    LDAPS_LH  ...  LDAPS_PPT2  \\\n",
       "0         28.074101         23.006936  6.818887   69.451805  ...         0.0   \n",
       "1         29.850689         24.035009  5.691890   51.937448  ...         0.0   \n",
       "2         30.091292         24.565633  6.138224   20.573050  ...         0.0   \n",
       "3         29.704629         23.326177  5.650050   65.727144  ...         0.0   \n",
       "4         29.113934         23.486480  5.735004  107.965535  ...         0.0   \n",
       "\n",
       "   LDAPS_PPT3  LDAPS_PPT4      lat      lon       DEM   Slope  \\\n",
       "0         0.0         0.0  37.6046  126.991  212.3350  2.7850   \n",
       "1         0.0         0.0  37.6046  127.032   44.7624  0.5141   \n",
       "2         0.0         0.0  37.5776  127.058   33.3068  0.2661   \n",
       "3         0.0         0.0  37.6450  127.022   45.7160  2.5348   \n",
       "4         0.0         0.0  37.5507  127.135   35.0380  0.5055   \n",
       "\n",
       "   Solar radiation  Next_Tmax  Next_Tmin  \n",
       "0      5992.895996       29.1       21.2  \n",
       "1      5869.312500       30.5       22.5  \n",
       "2      5863.555664       31.1       23.9  \n",
       "3      5856.964844       31.7       24.3  \n",
       "4      5859.552246       31.2       22.5  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 718,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('D:/数据分析/第八阶段/模块三/作业/Dataset.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "993c6e8e",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9486fbe6",
   "metadata": {},
   "source": [
    "**基本统计信息**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 719,
   "id": "38b0ab05",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <td>7750.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.211568</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Tmax</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Tmin</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>23.225059</td>\n",
       "      <td>2.413961</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>29.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>56.759372</td>\n",
       "      <td>14.668111</td>\n",
       "      <td>19.794666</td>\n",
       "      <td>45.963543</td>\n",
       "      <td>55.039024</td>\n",
       "      <td>67.190056</td>\n",
       "      <td>98.524734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>100.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>23.512589</td>\n",
       "      <td>2.345347</td>\n",
       "      <td>14.272646</td>\n",
       "      <td>22.089739</td>\n",
       "      <td>23.760199</td>\n",
       "      <td>25.152909</td>\n",
       "      <td>29.619342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>21.857621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.356080</td>\n",
       "      <td>0.258061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.312421</td>\n",
       "      <td>0.558694</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.318404</td>\n",
       "      <td>0.250362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101388</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.496703</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.254348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081532</td>\n",
       "      <td>0.227664</td>\n",
       "      <td>0.499489</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.591995</td>\n",
       "      <td>1.945768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052525</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.485003</td>\n",
       "      <td>1.762807</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018364</td>\n",
       "      <td>21.621661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.278200</td>\n",
       "      <td>1.161809</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007896</td>\n",
       "      <td>15.841235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>16.655469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solar radiation</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmax</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmin</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>22.932220</td>\n",
       "      <td>2.487613</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>29.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "station           7750.0    13.000000    7.211568     1.000000     7.000000   \n",
       "Present_Tmax      7682.0    29.768211    2.969999    20.000000    27.800000   \n",
       "Present_Tmin      7682.0    23.225059    2.413961    11.300000    21.700000   \n",
       "LDAPS_RHmin       7677.0    56.759372   14.668111    19.794666    45.963543   \n",
       "LDAPS_RHmax       7677.0    88.374804    7.192004    58.936283    84.222862   \n",
       "LDAPS_Tmax_lapse  7677.0    29.613447    2.947191    17.624954    27.673499   \n",
       "LDAPS_Tmin_lapse  7677.0    23.512589    2.345347    14.272646    22.089739   \n",
       "LDAPS_WS          7677.0     7.097875    2.183836     2.882580     5.678705   \n",
       "LDAPS_LH          7677.0    62.505019   33.730589   -13.603212    37.266753   \n",
       "LDAPS_CC1         7677.0     0.368774    0.262458     0.000000     0.146654   \n",
       "LDAPS_CC2         7677.0     0.356080    0.258061     0.000000     0.140615   \n",
       "LDAPS_CC3         7677.0     0.318404    0.250362     0.000000     0.101388   \n",
       "LDAPS_CC4         7677.0     0.299191    0.254348     0.000000     0.081532   \n",
       "LDAPS_PPT1        7677.0     0.591995    1.945768     0.000000     0.000000   \n",
       "LDAPS_PPT2        7677.0     0.485003    1.762807     0.000000     0.000000   \n",
       "LDAPS_PPT3        7677.0     0.278200    1.161809     0.000000     0.000000   \n",
       "LDAPS_PPT4        7677.0     0.269407    1.206214     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7725.0    30.274887    3.128010    17.400000    28.200000   \n",
       "Next_Tmin         7725.0    22.932220    2.487613    11.300000    21.300000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "station             13.000000    19.000000    25.000000  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "Present_Tmin        23.400000    24.900000    29.900000  \n",
       "LDAPS_RHmin         55.039024    67.190056    98.524734  \n",
       "LDAPS_RHmax         89.793480    93.743629   100.000153  \n",
       "LDAPS_Tmax_lapse    29.703426    31.710450    38.542255  \n",
       "LDAPS_Tmin_lapse    23.760199    25.152909    29.619342  \n",
       "LDAPS_WS             6.547470     8.032276    21.857621  \n",
       "LDAPS_LH            56.865482    84.223616   213.414006  \n",
       "LDAPS_CC1            0.315697     0.575489     0.967277  \n",
       "LDAPS_CC2            0.312421     0.558694     0.968353  \n",
       "LDAPS_CC3            0.262555     0.496703     0.983789  \n",
       "LDAPS_CC4            0.227664     0.499489     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.052525    23.701544  \n",
       "LDAPS_PPT2           0.000000     0.018364    21.621661  \n",
       "LDAPS_PPT3           0.000000     0.007896    15.841235  \n",
       "LDAPS_PPT4           0.000000     0.000041    16.655469  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  \n",
       "Next_Tmin           23.100000    24.600000    29.800000  "
      ]
     },
     "execution_count": 719,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "id": "edb325fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 25 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   station           7750 non-null   float64\n",
      " 1   Date              7750 non-null   object \n",
      " 2   Present_Tmax      7682 non-null   float64\n",
      " 3   Present_Tmin      7682 non-null   float64\n",
      " 4   LDAPS_RHmin       7677 non-null   float64\n",
      " 5   LDAPS_RHmax       7677 non-null   float64\n",
      " 6   LDAPS_Tmax_lapse  7677 non-null   float64\n",
      " 7   LDAPS_Tmin_lapse  7677 non-null   float64\n",
      " 8   LDAPS_WS          7677 non-null   float64\n",
      " 9   LDAPS_LH          7677 non-null   float64\n",
      " 10  LDAPS_CC1         7677 non-null   float64\n",
      " 11  LDAPS_CC2         7677 non-null   float64\n",
      " 12  LDAPS_CC3         7677 non-null   float64\n",
      " 13  LDAPS_CC4         7677 non-null   float64\n",
      " 14  LDAPS_PPT1        7677 non-null   float64\n",
      " 15  LDAPS_PPT2        7677 non-null   float64\n",
      " 16  LDAPS_PPT3        7677 non-null   float64\n",
      " 17  LDAPS_PPT4        7677 non-null   float64\n",
      " 18  lat               7752 non-null   float64\n",
      " 19  lon               7752 non-null   float64\n",
      " 20  DEM               7752 non-null   float64\n",
      " 21  Slope             7752 non-null   float64\n",
      " 22  Solar radiation   7752 non-null   float64\n",
      " 23  Next_Tmax         7725 non-null   float64\n",
      " 24  Next_Tmin         7725 non-null   float64\n",
      "dtypes: float64(24), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0591c1",
   "metadata": {},
   "source": [
    "**查看空值**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 721,
   "id": "0706d87d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "station              2\n",
       "Date                 2\n",
       "Present_Tmax        70\n",
       "Present_Tmin        70\n",
       "LDAPS_RHmin         75\n",
       "LDAPS_RHmax         75\n",
       "LDAPS_Tmax_lapse    75\n",
       "LDAPS_Tmin_lapse    75\n",
       "LDAPS_WS            75\n",
       "LDAPS_LH            75\n",
       "LDAPS_CC1           75\n",
       "LDAPS_CC2           75\n",
       "LDAPS_CC3           75\n",
       "LDAPS_CC4           75\n",
       "LDAPS_PPT1          75\n",
       "LDAPS_PPT2          75\n",
       "LDAPS_PPT3          75\n",
       "LDAPS_PPT4          75\n",
       "lat                  0\n",
       "lon                  0\n",
       "DEM                  0\n",
       "Slope                0\n",
       "Solar radiation      0\n",
       "Next_Tmax           27\n",
       "Next_Tmin           27\n",
       "dtype: int64"
      ]
     },
     "execution_count": 721,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 722,
   "id": "49a981f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>Present_Tmin</th>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>...</th>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "      <th>Next_Tmin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7/9/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.051193</td>\n",
       "      <td>99.668961</td>\n",
       "      <td>27.872808</td>\n",
       "      <td>22.907420</td>\n",
       "      <td>11.017837</td>\n",
       "      <td>44.002020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036680</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.785000</td>\n",
       "      <td>5925.883789</td>\n",
       "      <td>23.4</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7/10/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.196007</td>\n",
       "      <td>95.168205</td>\n",
       "      <td>28.097980</td>\n",
       "      <td>24.510159</td>\n",
       "      <td>8.374849</td>\n",
       "      <td>38.782242</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5102</td>\n",
       "      <td>127.086</td>\n",
       "      <td>21.9668</td>\n",
       "      <td>0.133200</td>\n",
       "      <td>5772.487305</td>\n",
       "      <td>26.1</td>\n",
       "      <td>24.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7/12/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.027298</td>\n",
       "      <td>99.209839</td>\n",
       "      <td>24.078120</td>\n",
       "      <td>21.866817</td>\n",
       "      <td>8.543768</td>\n",
       "      <td>9.371270</td>\n",
       "      <td>...</td>\n",
       "      <td>5.055660</td>\n",
       "      <td>1.347418</td>\n",
       "      <td>0.980052</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.785000</td>\n",
       "      <td>5893.265625</td>\n",
       "      <td>23.2</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7/18/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.891193</td>\n",
       "      <td>94.747780</td>\n",
       "      <td>29.195536</td>\n",
       "      <td>23.236973</td>\n",
       "      <td>10.881031</td>\n",
       "      <td>79.349271</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.057358</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.785000</td>\n",
       "      <td>5812.293457</td>\n",
       "      <td>27.6</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>15.0</td>\n",
       "      <td>7/18/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.795406</td>\n",
       "      <td>83.902847</td>\n",
       "      <td>31.480089</td>\n",
       "      <td>25.607262</td>\n",
       "      <td>8.995135</td>\n",
       "      <td>26.022306</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008702</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>126.937</td>\n",
       "      <td>30.0464</td>\n",
       "      <td>0.855200</td>\n",
       "      <td>5681.875000</td>\n",
       "      <td>30.7</td>\n",
       "      <td>23.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7629</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8/26/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.755058</td>\n",
       "      <td>83.340240</td>\n",
       "      <td>25.842338</td>\n",
       "      <td>18.532986</td>\n",
       "      <td>4.926595</td>\n",
       "      <td>97.230757</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.505500</td>\n",
       "      <td>4602.118164</td>\n",
       "      <td>26.1</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7682</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8/28/2017</td>\n",
       "      <td>26.3</td>\n",
       "      <td>18.1</td>\n",
       "      <td>29.959215</td>\n",
       "      <td>90.116638</td>\n",
       "      <td>23.135079</td>\n",
       "      <td>17.282587</td>\n",
       "      <td>9.292264</td>\n",
       "      <td>75.430868</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.4697</td>\n",
       "      <td>126.910</td>\n",
       "      <td>52.5180</td>\n",
       "      <td>1.562900</td>\n",
       "      <td>4518.488770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7707</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8/29/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.392651</td>\n",
       "      <td>75.728195</td>\n",
       "      <td>22.223247</td>\n",
       "      <td>15.954970</td>\n",
       "      <td>4.764492</td>\n",
       "      <td>37.786237</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.4697</td>\n",
       "      <td>126.910</td>\n",
       "      <td>52.5180</td>\n",
       "      <td>1.562900</td>\n",
       "      <td>4478.937012</td>\n",
       "      <td>23.1</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>19.794666</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>14.272646</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.4562</td>\n",
       "      <td>126.826</td>\n",
       "      <td>12.3700</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>17.4</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.6</td>\n",
       "      <td>29.9</td>\n",
       "      <td>98.524734</td>\n",
       "      <td>100.000153</td>\n",
       "      <td>38.542255</td>\n",
       "      <td>29.619342</td>\n",
       "      <td>21.857621</td>\n",
       "      <td>213.414006</td>\n",
       "      <td>...</td>\n",
       "      <td>21.621661</td>\n",
       "      <td>15.841235</td>\n",
       "      <td>16.655469</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.135</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>5.178230</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>38.9</td>\n",
       "      <td>29.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      station       Date  Present_Tmax  Present_Tmin  LDAPS_RHmin  \\\n",
       "225       1.0   7/9/2013           NaN           NaN    70.051193   \n",
       "271      22.0  7/10/2013           NaN           NaN    72.196007   \n",
       "300       1.0  7/12/2013           NaN           NaN    95.027298   \n",
       "450       1.0  7/18/2013           NaN           NaN    60.891193   \n",
       "464      15.0  7/18/2013           NaN           NaN    52.795406   \n",
       "...       ...        ...           ...           ...          ...   \n",
       "7629      5.0  8/26/2017           NaN           NaN    43.755058   \n",
       "7682      8.0  8/28/2017          26.3          18.1    29.959215   \n",
       "7707      8.0  8/29/2017           NaN           NaN    44.392651   \n",
       "7750      NaN        NaN          20.0          11.3    19.794666   \n",
       "7751      NaN        NaN          37.6          29.9    98.524734   \n",
       "\n",
       "      LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_Tmin_lapse   LDAPS_WS    LDAPS_LH  \\\n",
       "225     99.668961         27.872808         22.907420  11.017837   44.002020   \n",
       "271     95.168205         28.097980         24.510159   8.374849   38.782242   \n",
       "300     99.209839         24.078120         21.866817   8.543768    9.371270   \n",
       "450     94.747780         29.195536         23.236973  10.881031   79.349271   \n",
       "464     83.902847         31.480089         25.607262   8.995135   26.022306   \n",
       "...           ...               ...               ...        ...         ...   \n",
       "7629    83.340240         25.842338         18.532986   4.926595   97.230757   \n",
       "7682    90.116638         23.135079         17.282587   9.292264   75.430868   \n",
       "7707    75.728195         22.223247         15.954970   4.764492   37.786237   \n",
       "7750    58.936283         17.624954         14.272646   2.882580  -13.603212   \n",
       "7751   100.000153         38.542255         29.619342  21.857621  213.414006   \n",
       "\n",
       "      ...  LDAPS_PPT2  LDAPS_PPT3  LDAPS_PPT4      lat      lon       DEM  \\\n",
       "225   ...    0.036680    0.000000    0.000000  37.6046  126.991  212.3350   \n",
       "271   ...    0.007261    0.000000    0.000000  37.5102  127.086   21.9668   \n",
       "300   ...    5.055660    1.347418    0.980052  37.6046  126.991  212.3350   \n",
       "450   ...    0.000000    0.000000    0.057358  37.6046  126.991  212.3350   \n",
       "464   ...    0.000000    0.000000    0.008702  37.5507  126.937   30.0464   \n",
       "...   ...         ...         ...         ...      ...      ...       ...   \n",
       "7629  ...    0.000000    0.000000    0.000000  37.5507  127.135   35.0380   \n",
       "7682  ...    0.000000    0.000000    0.000000  37.4697  126.910   52.5180   \n",
       "7707  ...    0.000000    0.000000    0.000000  37.4697  126.910   52.5180   \n",
       "7750  ...    0.000000    0.000000    0.000000  37.4562  126.826   12.3700   \n",
       "7751  ...   21.621661   15.841235   16.655469  37.6450  127.135  212.3350   \n",
       "\n",
       "         Slope  Solar radiation  Next_Tmax  Next_Tmin  \n",
       "225   2.785000      5925.883789       23.4       22.0  \n",
       "271   0.133200      5772.487305       26.1       24.1  \n",
       "300   2.785000      5893.265625       23.2       20.5  \n",
       "450   2.785000      5812.293457       27.6       21.8  \n",
       "464   0.855200      5681.875000       30.7       23.4  \n",
       "...        ...              ...        ...        ...  \n",
       "7629  0.505500      4602.118164       26.1       17.9  \n",
       "7682  1.562900      4518.488770        NaN        NaN  \n",
       "7707  1.562900      4478.937012       23.1       16.3  \n",
       "7750  0.098475      4329.520508       17.4       11.3  \n",
       "7751  5.178230      5992.895996       38.9       29.8  \n",
       "\n",
       "[164 rows x 25 columns]"
      ]
     },
     "execution_count": 722,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出所有有空值的记录\n",
    "# data.isna().T.any()\n",
    "data.loc[data.isna().T.any()==True]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c1f96f1",
   "metadata": {},
   "source": [
    "**查看每列数据的分布状态**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "id": "67a44b42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='station', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Present_Tmax', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Present_Tmin', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_RHmin', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_RHmax', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_Tmax_lapse', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_Tmin_lapse', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_WS', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_LH', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_CC1', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Kq5v5xuNb6LChqMb0i69NRguAnwPfAx7H883+v328dqmI3Nbl9LeBPOA+74iic4A7gVeBt4AHVXWvb7cQ3spqmgFLCP4yd1Qq37tkCq/sruDBNwqdDseYsOJrL+YtwB+BO1Q9G9uKyLd6u0BVl3h/rgPWdXnuW0B310/2MZ5Bo6y2mQiXMCze2U1oBpPPnzma/EPH+dlLe5k9IoUzxw93OiRjwoKvTUafV9V1nZLBWO8HvRmg0pomMpNicLtsyWt/OTkUdWx6Arf8+T0KKmx+gjG+8LXJ6JEup/4UgFiGHFWltKbZmosCICE6goc+fzqRbuHzD71DRV2z0yEZE/J6TQjeGcXnANNE5Gzv4yLAhnD4QUNrO42t7TbCKEBGpsXx+8+fTlV9Kzf9IZ+GljanQzImpPVVQxgDLAFSvT/PxTOr+MaARjVEnPzWmh7kLTOHkpkjUrj/ujnsLKnhy3/aTPOJdqdDMiZk9dqprKqvA6+LyChVvStIMQ0ZlXUtAEHfQ3moOW9KJvdcPZNvPrmNm/+Yz28/O4+YSLfTYRkTcnwaZeSdh2D8rLKuhUi3kBQb6XQoYc3X1VuvmjOCp94v4qaH3+V3nz2d2ChLCsZ0ZvshOKiyroX0xGhcYiOMgmHuqFR+ds0sNhVW8bmH3rHZzMZ00Ven8je9Px8Skd93fgQnvMGtsq6F9ARrLgqmq+eO4FefmsOWw9Vc/eAmjhxrdDokY0JGX01GD3t//jDAcQw5rW0dVDedYF6ijTAKtstm5ZCRGM2KP+Zz5QOb+P3n5zFzRIrTYRnjuL46lcu9Pw8FJ5yho7Le06FsI4yCq3N/w41njeHhNw9y1QObuGJ2Lqd12rFuMOw7bUx/9bsPQUTGiIj1PQyQjTByXkZSDLcsGU9eWhxPvFfEM1uKabMNdswQ5tMoIxFZCbwCzATOA8qAawIY16BXWdeMAMPio5wOZUhLiI7gC2eN4eVd5byxr5KS6iaum2+1AzM0+fpNf5qqPgksVNVFQE4AYxoSKutaSIuPIsJtlS2nuV3ChdOzuG5+HpV1Ldz/agEb9h11Oixjgs7XT6M2Efk/YJ939zQbrzdAFd4hpyZ0TM9N5itLxpMQHcENv3+b+/65z/ZUMEOKrwnhWuAN4D+ABOCzAYtoCGhr76CqodX6D0JQemI0tywZx2Wzcvj5yx9w08PvUt3Y6nRYxgSFrwmhFigBTgfagFEBi2gIOHK8ifYOtRpCiIqOcPN/187mR5dPY0PBUS65dwPbiqqdDsuYgPM1IfwT+Ayexe3OxbPQnTlFhd71+dNtDkLIEhFuOGM0a750BqrKNQ++yfPbSp0Oy5iA8jUhdKjqbap6p/fR60J3IpIpIut7eT5SRJ4TkU0icmNP5wargkpvQrBZyiFvTl4qf799ETNyk7n10ff4zeuFePeJMmbQ8XULzZdF5H/wzFxuAFDVblcUE5FUb7n4Xl7vdiBfVX8oIk+JyOPAzV3PqWqdz3cSRgor6kmMjrDF1cLEsIRo/vzFBXxjzVZ+snYPR443cudl0/+1y52vi+vZZDcT6nxNCGO9P7/p/an0vCdCO55O6L/18npLgG97jzcB83o492rni0RkBbACIC8vfP9zFVbWM9z6D8JKTKSb+z49hxFpsfzm9f20nOjgnqtn4rKtT80g4uvy11/wfvPPAY4D5b2UrQVPG2wv4oFi73EtkNnDua6vvQpYBTBv3rywrLerKgUV9UzJTnI6FNOLnr71j0qLZ+nkDB7fXMSBow1cMSfXVqs1g4aveyp/C1gLPIbnm/xDA3zfeiDWe5zgjaO7c4PO0fpWapvbbIRRGDtvcgZLJqWTf+g4z24tsT4FM2j4+qF7qaouBKpU9VE+bEI6VZuBRd7jWcDBHs4NOgX/GmFkCSFciQjLpmRy9oR03jlwjFf3VjodkjF+4WsfQq2IfBaIEZFzgGpf30BElgJTVfX+TqcfBl4QkcXAVOBtPM1FXc8NOoU2wmhQEBGWT8uktvkEr+wuZ3hClC2hbcJenzUEEZkOvAWsBuYD36LnDuV/UdUl3p/ruiSDk8tpLwM2Auerant35/p3K+GhoKKeuCg3ybZtZtgTEa6ak8uotDie2FxE0XHbbMeEt752TPsinr6DHOCnwG/xfHs/Z6BvrKolqrpGVWt6OzfYFFbWMy49oa9OdxMmItwurl84isSYCB558xC1TbbMlwlffdUQVgCzVPXLqvo9Vf0yMBv4esAjG6QKK+oZn5HgdBjGjxKiI/jsGaNpbmvnr/lH6LBOZhOm+koIkcAkETnz5ANPDcEawE9BQ0sbJTXNjEvvbc6eCUeZSTFcPjuXA0cbWLenwulwjDklfXUqb8E7EayLbf4PZfDbX9kAwPiMBI41WNPCYHNaXir7K+t5dU8FY4bHMy7daoImvPS1p/IXghXIUHByhNG49ASONRx3OBoTCJfOyuHIsSbWvHuE28+bQEK0rwP5jHHeoJz8FaoKKupxu4RRw6zJaLCKjnDz6fl5NJ1o56n3imzSmgkrlhCCqLCynlFpcURF2B/7YJaVHMPyaVnsKavjnYPHnA7HGJ/ZJ1MQFVTUM85GGA0JZ4wbxviMBF7YXkplXYvT4RjjE0sIQdLW3sHBqgbraBwiXCJcc9oIIt0u1uQfoa2jw+mQjOmTJYQgOXyskRPtanMQhpCk2EiumJ1LcXUT63bbUFQT+mwIRJAUeoec2hyEoWV6bjJzR6Xy+geVvHPgGPPHpDkdkt/YxkCDj9UQguTkKqfWhzD0fGJmNqnxUXz9r1uobbb5JyZ0WQ0hSAor68lIjCYpxha1G2qiI9x8ct5Ifrt+Pz/4205+ee1sp0MKKl9rEmC1CadZQgiSAlvDaEjLS4vj9qXj+b9X9nHW+OFcM3eE0yEFRYcqpTXNlNU0U93USkeHEh8dQUZiDKOGxRHptkaKUGIJIQhUlcLKeq6Ynet0KMZBt507nrf2V/Hdp7czMTNhUO+fUF7bzDsHj7G9qIb6lrZuy0S5XcwckczZE9MZ7t0fxPolnGUJIQgq61qoa26zGsIQF+F28evrTuOy+zfypUc28+xtiwbdznmlNU28sruC3aW1uF3ClKxEJmcnkZcWR2pcFC6B+pY2Sqqb2FlSy5Yj1bx3+DiLJ6SzdHKG1RgcZgkhCAo6rWFkhrZhCdH85oa5XPPgJm7983v8+eYFg+JDsLG1jVd2l/P2/mPERLo5b0oGC8cMI76btZwSYyKZlBXJpKwklk3N5B87y3n9g0o+KK/jMwtHkRoX5cAdGAhQQhCR1cAU4AVVvbub528BrvX+moJnu8xbgf3eB8Dtqro9EPEFW6F3hJHVEAx4hqLec/VM7vjLFv7j8a384pOzcblObcMkp5tYOlTJP3icf+wqo6m1nQVjh7FsSiaxUW6frk+MieTquSOYlpvEmvwjPPBqAZ8/awy5KbEBidf0zu9fTUTkKsCtqmcCOSIyoWsZVV2pqku822yuB1YBM4HHTp4fLMkAPHMQEqIjyEwaXM0D5tRdPjuX/1g+iWe2lPDdp7fT3hF+i+Adrmpg5WuFPLOlmIzEaG5bOp7LZuX4nAw6m5yVxC3njCcywsXqDfs5csy2I3VCIOqqS4A13uN1wKKeCopILpCpqpuBhcCVIrJBRP4sIh+rvYjIChHJF5H8ysrKAIQeGAUV9YxLj7dtM81H3HrueG5fOp6/vHuE2x97j+YT4bGNeEl1E1//6xYefGM/dc0nuHbeSG5ePJbs5IF9q09PjObmxWOJi4rgD5sOUlHb7KeIja8CkRDigWLvcS2Q2UvZW4GV3uN3gXNUdRFQDVzctbCqrlLVeao6Lz093X8RB9jJfZSN6eobF0zi+5dM4YXtZVz1wKZ/TWAMRXXNJ/jpi3s492ev8fz2Us6ZmM7Xl01k1sgUv33ZSY2L4sazxuB2CX/YdNAm8gVZIBJCPXDyq0JCT+8hIi7gXFV91Xtqm6qWeo/3AB9ragpH9S1tlNY02wxl06MvLh7L6s/No7SmiYvvXc89L+6hurHV6bD+5eDRBu78+07O/Mk6HnitkIumZ7HuG+ewfFoW0RH9bx7qS1p8FJ87czQNrW385Z3DYdmcFq4C0am8GU8z0VvALGBvD+UW4+lMPukREfkxsAO4EvjvAMQWdPtthJHxwXlTMnnxa2fzP2v3sPK1QlZvOMAFUzNZNH44k7OTSE+MpqNDaetQWtraOVbfSmV9CxsLjlLf0uZ5NLfR0NrGifYO3C4hwuUiOsJFcmwkFXXN5CTHkp0SQ05KLLkpscREdv9h3t6hfFBex9v7q3h5dzmbCquIcAmXzMjmi4vHMj03OeB/HrkpsVw5J5c1+UX8Y2cZF83IDvh7msAkhGeA9SKSA1wEfEpE7lbV73cptxx4o9PvdwGPAgI8q6qvBCC2oCuwEUbGR5lJMfzy2tmsOHssj759mLU7SnluW2mf17kEEqIjiI+OICE6gqSYSNo7lPYOpbG1ndKaZvIPfXzL1uEJUSTFRhIX5SYuMoITHR1UN56g+HgTre2e5brHpsfz1aUTuH5BHhlJMX6/597MHpnKoapG1hccZWRaXFAS0VDn94SgqrUisgRYBvxUVcuArd2U+26X33fgGWk0qBRW1hPhEkYNi3M6FBMmpmQn8aMrpnPX5dPYf7SBA5UNHK1vweUSIt1ClNtNWnwU6YlRvLa3kphIN64+2vCvnptLeU0LJTVNlFR7HsXVTdQ2tdHY2kZjazsJ0RHkJMdywbRMJmUmMndUquPbvV4yI5vi6iaefK+I7OQYhiXYSL1ACsg8BFU9zocjjYa0gop68mzNFnMKRIRx6Qm9Nje+c+Dj3/y7Ex3hJm9YHHlh9sUkwu3iuvl53LtuH49vLmLF2WP7TH7m1NlM5QDbV17PxMxEp8MwQ1w4rziaEhfFZbNyWZN/hPUfVHLOpAynQxq07GtrADWfaOdgVQMTM63/wJiBmDUimek5Sbyyu4LSmianwxm0rIYQQPsrG+hQmGA1BNMP/fk2P1SICJfPzuVg1T4ezy/itqXjAzLkdaizGkIA7auoA2CC1RCMGbD46AiumpNLWW0z968rcDqcQclqCAG0r7wet0sYM9z2UTbh880/lOOcnJ3EnJEprHytkItnZDMlO8npkAYVqyEE0AfldYweFmdVW2P86JIZ2STHRvLtJ7fZLGY/s4QQQPsqbISRMf4WFx3BDy6bxtaiGh7aeMDpcAYVSwgB0nyinUNVDdahbEwAXDozm/MmZ/Czf+zlcJUtle0vlhACpLCyng7FhpwaEwAiwt1XTifC5eK7T29H1ZqO/MESQoDsK/esYWRNRsYERnZyLN+6aDIbCo7y+OYip8MZFCwhBMgH5XVEuITRDq8FY8xgdv38POaPTuPu53ZRUWcb6gyUJYQA+aC8njHD44mKsD9iYwLF5RJ+cvUMmts6+OGzO50OJ+zZp1WA7KuoswlpxgTBuPQE7jhvAi9sL+OlnWVOhxPWLCEEQPOJdg4fa2RChvUfGBMMK84ey5TsJP7zmR3UNNm2m6fKEkIAFFTUo2odysYES6TbxT1Xz+BofQv/s3a30+GELUsIAXByDSMbcmpM8MwckcIXF4/lsXeO8Nb+KqfDCUsBSQgislpENolI120zTz4fISKHReQ172OG9/ydIvKuiNwfiLiC5YPyeiLdwmhbw8iYoPr6+RMZmRbLd5/eTvOJdqfDCTt+TwgichXgVtUzgRwRmdBNsZnAY6q6xPvYLiLzgEXAfKBIRM73d2zBsq+8jjHD422XNGOCLDbKzY+vmMH+ygYeeK3Q6XDCTiBWO13Ch9tnrsPzIb+vS5mFwJUichZwCPgccDbwpKqqiLwCXAq80vkiEVkBrADIywutXZ06211ax9xRqU6HYcyg1deKrLNHpvDrdQUI8PVlE4MT1CAQiK+w8UCx97gWyOymzLvAOaq6CKgGLvblOlVdparzVHVeenq6v+P2i+rGVoqrm5iaY8vyGuOUi2dkExXh4pktxXTYiqg+C0RCqAdivccJPbzHNlUt9R7vASb4eF3I21VaC8BUW6fdGMckREdw8YxsDlU18pd3jzgdTtgIxIfuZjzNRACzgIPdlHlERGaJiBu4Etjq43Uhb1eJJyHYxh3GOOu0vBTGDo/nJ2t3U1Fry1r4IhB9CM8A60UkB7gI+JSI3K2qnUcc3QU8CgjwrKq+IiIu4Cci8ivgQu8j7OwqrSUjMZr0xGinQzFmSBMRrpiTy73/3MeND+dz3fze+x2vWxC6/ZLB4veEoKq1IrIEWAb8VFXL8NQAOpfZgWekUedzHd6RRZcAv1LVsNz5YldJrfUfGBMihidEc+7kDF7eVc6e0lomW829VwFpp1fV46q6xpsM+nNdk6o+oar7AxFXoLW0tVNQUW/9B8aEkMUThpORGM3ftpbQ0mZzE3oTlh23oWpfeT1tHWo1BGNCSITLxVVzcqltOsEru8qdDiekWULwo+3FNQBMy0l2OBJjTGd5w+KZPyaNTYVVFB23LTd7YgnBj7YVVZMUE8HoYXFOh2KM6WL5tCwSYyI8cxNsy81uWULwo61Hapg1MgURcToUY0wXMZFuLp6RTUl1M+8ePOZ0OCHJEoKfNJ9oZ295HTNHWHORMaFqRm4yY4bH84+d5TS2tDkdTsixhOAnO0tqae9QZo5IcToUY0wPRIRLZ+XQ0tbOP6yD+WMsIfjJtqJqAGZZQjAmpGUlxXDG2GG8e/AYxcebnA4npFhC8JOtR6rJSIwmKznG6VCMMX04b0omcdERPLvVOpg7s4TgJ1uOVDNrZIrTYRhjfBAT6eaiaVkcOd7E+4ernQ4nZFhC8IPKuhYOVjVy+mjbA8GYcDE7L4W8tDhe3FFKU6vNYAZLCH6x+ZBnCNu80WkOR2KM8ZXL28Hc2NrOuj3WwQyWEPzi3YPHiY5wMd1mKBsTVnJTYpk3OpU391dRUFHndDiOs4TgB/kHjzF7ZApREfbHaUy4WTY1i6gIF3f+fRc6xDuY7RNsgBpb29hRUsvp1lxkTFhKiI7gvMmZrN93lFd2VzgdjqMsIQzQ+4erae9Q5lmHsjFha+HYYYzPSODu53cN6SWyLSEM0MaCo0S4hLmjLCEYE67cLuG/PjGVQ1WNrN4Qlntz+UVAEoKIrBaRTSLy/R6eTxaRtSLysog8LSJRIhIhIodF5DXvY0YgYvO3DQVHmZOXQmJMpNOhGGMG4OyJ6Zw/JZP71xVQPkT3YPZ7QhCRqwC3qp4J5IjIhG6KXQ/8QlWXAWV49k+eCTymqku8j+3+js3fjje0sr24hkXj050OxRjjB//5iSm0tSv3vLjH6VAcEYgawhJgjfd4HbCoawFVfUBVX/b+mg5UAAuBK0Vkg4j8WUQ+tt+ziKwQkXwRya+srAxA6P2zsfAoqrBownCnQzHG+MGoYfHctHgMT71XzPuHjzsdTtAFIiHEA8Xe41ogs6eCInIGkKqqbwHvAueo6iKgGri4a3lVXaWq81R1Xnq689/KN+w7SmJMBLNsyWtjBo1bzx1PRmI0P3x2Jx0dQ2sYaiASQj0Q6z1O6Ok9RCQNuA+40Xtqm6qWeo/3AN01NYUMVeX1Dyo5Y+wwItzWN2/MYJEQHcG3L5rM1qIannyvyOlwgioQn2Sb+bCZaBZwsGsBEYnC06z0HVU95D39iIjMEhE3cCWwNQCx+c324hpKa5pZNrXHCpAxJkxdMTuX2SNTuOfFvdQ1n3A6nKAJREJ4BrhBRH4BfBLYKSJ3dylzEzAX+J53RNG1wF3AI8AW4E1VfSUAsfnNSzvLcLuE86dYQjBmsHG5hB9eNo2j9S3cv67A6XCC5mMdtwOlqrUisgRYBvxUVcvo8m1fVVcCK7u5fKa/4wmUF3eUsWBMGqnxUU6HYowJgNkjU/i3uSNYveEAV56Wy+SsJKdDCriANH6r6nFVXeNNBoNOQUU9hZUNXDg9y+lQjDEB9J2Lp5AUG8m3n9xO+xDoYPZ7DWEo+PvWEkTggqmWEIwZLB59+3C358+fksma/CN89bH3OWv8cK5bkBfkyILHhsf0U0eH8sTmIhaNH27bZRozBMwakcykzERe3lXO8YZWp8MJKEsI/fTm/iqKq5v4t3kjnQ7FGBMEIsLls3NA4JktxYN6iWxLCP20Jv8ISTERXGDDTY0ZMlLiorhwWhb7Kup5aONBp8MJGEsI/VBR28zaHWVcMSeXmEi30+EYY4JowZg0pmQl8pO1u9lWVO10OAFhCaEffr/xIG3tHdx41hinQzHGBJmIcPXcEaQnRHPbo+9TOwgnrFlC8FFt8wn+/NYhLp6Rzejh8U6HY4xxQFxUBPd+eg7F1U1856ntg64/wRKCj36/4QB1LW18+ZxxTodijHHQvNFpfOOCiTy/rZQHXit0Ohy/snkIPig63sjK1wq5ZGY203NtZVNjhrpbzhnH3rI6/velvYxIjeXy2blOh+QXlhB88N8v7EYEvnvxFKdDMcaEABHhp9fMpLSmmX9fs5XYSDcXTAv/iarWZNSHp98v4oXtZdy6ZDy5KbF9X2CMGRKiI9ys/tw8ZuQmc+uj7/HcthKnQxowSwi9KKio43tP72D+6DRuWWJ9B8aYj0qMieThG+cze2QKtz/2Pr9bvz+sO5otIfTgyLFGblj9DrGRbn716dm2CY4xplvJsZE8ctMCLpiayd3P7+b2x96npik8h6Tap1w3dpXU8qlVb9HY2s4fb5pPdrI1FRljehYT6Wbl9XP55oWTWLujjAt++TovbC8Nu9qCJYRO2to7+MPGA1y1ciNtHR386aYFTMuxUUXGmL65XMJXlozn6a+cSWpcFF/583tctXITL+8qD5u9mW2UEZ5JZ89uKeGhjQcorGxg8YTh/OKTs0lPjHY6NGNMmJk5IoXnbl/EE5uLuPef+7j5j/lkJ8dwwdRMlk/LYv6YtJBtgg5IQhCR1cAU4AVV7bp9Zo9lfLluoJpPtLOrtJYPyurYW17HzpJa3jt0nLYOZWp2Er+5YS4XTM1ERALx9saYISDC7eJT8/O4eu4IXtpZxt+2lPCXd4/w8JuHiIl0MTkriak5SUzISCA7OYbMpBiGJ0QTHx1BfLSbKLfLkc8gvycEEbkKcKvqmSLygIhMUNV9fZUBZvR1nT/sLKnh6pVvAhAb6WZiZgI3nz2W5dOymDUi2RKBMcZvIt0uPjEzh0/MzKGxtY03PqjknQPH2VVaw3NbS6htbuv2ugiXEBvlSQxul+B2CS6Rfx2fOymD/7p0qt/jFX93eojIvcCLqvqCiFwDJKrqQ32VAeb4cN0KYIX310nAXr8GH1zDgaNOB+EHg+E+BsM9wOC4D7uHwBulqundPRGIJqN4oNh7XAuM97FMn9ep6ipglT+DdYqI5KvqPKfjGKjBcB+D4R5gcNyH3YOzAtGzUQ+cHKeZ0MN7dFfGl+uMMcYESCA+dDcDi7zHs4CDPpbx5TpjjDEBEogmo2eA9SKSA1wEfEpE7lbV7/dSZiGg3ZwbzAZF0xeD4z4Gwz3A4LgPuwcH+b1TGUBEUoFlwBuqWuZrGV+uM8YYExgBSQjGGGPCj3XcGmOMASwhBJyIrBaRTSLy/YGUcVJf8YlIsoisFZGXReRpEYkKdoy+8PXPWUQyReT9YMXVH/24hwdE5NJgxdVfPvybShWRF0RkvYg8GOz4fOX9t7K+l+cjReQ5773eGMzYToUlhADqPCMbyPHOyO53GSf5GN/1wC9UdRlQBlwYzBh90c8/55/x4RDokOHrPYjIYiBLVf8e1AB95ON93AD8SVUXA4kiEnLj+r19ng/jmUPVk9uBfO+9fkJEEoMS3CmyhBBYS4A13uN1fDistr9lnLSEPuJT1QdU9WXvr+lARXBC65cl+PDnLCJLgQY8iS3ULKGPexCRSOC3wEERuTx4ofXLEvr+u6gCJolICjASOByUyPqnHbgWz0Tanizhw3vdBIRcYuvMEkJgdZ19nXmKZZzkc3wicgaQqqpvBSOwfurzPrxNXf8FfDuIcfWHL38XnwV2AT8F5ovI7UGKrT98uY8NwATgq8Ae4HhwQvOdqtaqak0fxUL9//dHWEIIrFOdtR1KfIpPRNKA+4BQbSf15T6+DfxaVauDFVQ/+XIPc4BV3mHbfwLODVJs/eHLffw38GVVvQtPQvhCkGLzt1D///0RIR3cIHCqs7ZDSZ/xeb9ZrwG+o6qHghdav/jy53w+cKuIvAbMFpHfBSc0n/lyDwXAWO/xPCAU/z58uY84YIaIuIEFeCauhqNQ///9UapqjwA9gCRgK/ALYDeefxB391Em2em4T+EebsFTpX/N+7jW6bhP5T66lH/N6ZhP8e8iEXgceAN4E8h1Ou5TvI/5wE4837BfBhKcjruvfyvAUuC2Ls+N8t7Hr4B38XSmOx5zTw+bmBZgpzprO5SEeny+Ggz3MRjuAQbPffjCuxzPIuAl7bvPwVGWEIwxxgDWh2CMMcbLEoIxxhjAEoIxxhgvSwjGGGMASwhmiBORH4rIZzr9/gcR2SIi+SJyc6fzs0XkQJdy73sXX/u7iCSISLaIvORdyOx/enlPt4is8l77sIi4ujvnLTtFRP4WqPs3pjNLCMZ83G3AcuAHIjLTe245MEJEJnYqd7t6Fl97G88Cf3cAq9WzkNlsEcnq4fWvBaK915YBV3R3TkTGAf8LJPv17ozpgSUEY7qhqlXA88DZ3lPLgV/T/UquqUATnjVrPiMiuap6YS/j65d7Xxvgr0BlD+fqgKsHeCvG+MwSgjE9qwJSRCQBGAb8Ds8H90n3edfCjwMeA+4H1gKvish3e3ndTOAYgKq+p6rruzunqhWq2uLvmzKmJxFOB2BMCEsDivAsSTAMzwf+TBGJ9j5/u6puOFlYRGYAq4E/AC+KyEZVfb2b163Fs9AZInKF9/hj51T1T/6/JWN6ZjUEY7rhXYf/Ijzr9S8HvqqqS/A06yzu4bLvA2eoahPwARDTQ7mNeJZtwPuzuodzxgSV1RCMgbtE5Gve49F4FltrAb6lqntEZBnwn97n19HzjnA/AlaJSBuwH8+ibN1ZBfxeRDYAB4AXgOhuzhkTVLaWkTHGGMBqCMYEjHfY6V+6nN6rql9yIh5j+mI1BGOMMYB1KhtjjPGyhGCMMQawhGCMMcbLEoIxxhgA/j9/hXpzmiXhzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_CC2', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_CC3', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_CC4', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_PPT1', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_PPT2', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_PPT3', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_PPT4', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='lat', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='lon', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEM', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FRPxIwRHCthyoJS4mitzeicEupVNOy+lFlMGakuqOG4tI2FBwhCjnHFv3H2JIRhIxUeH5NCX1iCEvK5k1e6rUXSUSQcLzHakbqDh8jMojDQwL026qVmP7p1J1pIHdlUeDXYqI+ImCI0RtOdByj4vhWclBruTUjM7uRXSUsW6PuqtEIoWCI0Rt3X+ItMRY0rvgavFAio+NZkhGEptKDwW7FBHxEwVHCGpsbmb7wcMM69MzIJMadrWRfXtysLaeg7X1wS5FRPxAwRGCiiuOcKyxOey7qVqN6Nty1ftmHXWIRAQFRwjaur+WKIMhmZERHOlJcWT17MHG0ppglyIifqDgCEFbDxwiNz3RL7eIDRUj+/Zi58HD1NQ1BLsUETlFCo4Qc7i+kb1VdeRlhfdpuG2N7NuTZgcLt2jGXJFwF5DgMLPZZrbIzGb50sbM+pjZwuO+jjWzf3ra3RKIWkPNjoOHARiamdRBy/AyID2RhNho/rVpf7BLEZFT5PfgMLMrgGjn3BQgx8yGedPGzNKAJ4Dj3zG/CSz3tLvEzCLr3/B27Dh4mNhoo19aQrBL8avoKGNE354s2FxGU7OuIhcJZ4G4A2Ah8JxneT4wFdjqRZsXgauBv7dp95+e5UVAPvDO8Rsys5nATIDc3PC/VemOg4cZ2Pvk04z4civXQOlMDSP79mTV7ipW7a5iwsA0vzzmdZNzfW4TCfy5n93ldyb+E4iuqiRgj2e5BujjTRvnXI1zru3lxR1uyzn3qHMu3zmXn5mZecrFB9Ph+kZKa+oYkhFZ3VSthmX1JDrK+NdGdVeJhLNABEct0NrPknyCx/CmjS/tIkLr+MbgCA2OhLhoJgxMY8HmsmCXIiKnIBBvxEW0dD0BjAN2drKNL+0iQqSObxzvvBFZbNhXw/4a3aNDJFwFIjjmATea2W+ALwHrzey+Dtq8coJtPQH8xMx+B4wGlgSg3pDhzfhGuCsc0dKd+K6OOkTClt/foZxzNbQMai8GznPOrXbOzeqgTfVx3ys8bnkXMB34APisc67J3/WGiorDxyJ6fKPVyL496dsrngVbDgS7FBHppECcVYVzrpKPz5rqdBtPu73etAt3S3eUA5E7vtHKzCgckckra/bR0NRMbHTkHl2JRCq9akPE4u0VET++0apwRBaH6htZsasy2KWISCcoOELE4u3lET++0ersvN7ERBnvaJxDJCxF/rtUGKg4fIxNpYcivpuqVc/4WCYOSmfBZo1ziIQjBUcIaB3fiPSB8eMVjshkU+kh9lXrXuQi4UbBEQIWb68gPjaqW4xvtDpvZBag03JFwpGCIwQs3l5O/sD0bjG+0WpYVjI5KfG8o+4qkbDTfd6pQlSlZ3xj8uD0YJfSpcyMwpFZfPBROccam4Ndjoj4QMERZEt3VgAweUjvIFfS9QqHZ1Jb38jyXRXBLkVEfKDgCLLF28vpERPFuAEpwS6ly52dl0FstGmcQyTMKDiCbMn2CsbnptEjJnLuL+6tpB4xTBqcrnEOkTCj4Aii6iMNbCytoaAbdlO1Om9EFlv217K74kiwSxERLyk4gmjpzgqcg8lDutfA+PGmj265N9cb60uDXImIeEvBEURLtpcTFxPFGQNSg11K0AzsncSo7F68tk7BIRIuFBxBtHhHOWcOSCU+tvuNbxxvxul9KdpVyQHd3EkkLCg4gqT6aAMb9nbv8Y1WM07vC6i7SiRcKDiCZPnOCpq7+fhGq2F9epKXlcw/Vu8Ldiki4gUFR5As2VFBXHQU43PTgl1KSPjCGTks3Vmhs6tEwoCCI0gWby/nDI1v/Nvnz+gHwN9X7QlyJSLSEQVHEByqa2DdnmoK1E31bwPSE5k0OJ2XVu7BORfsckTkJBQcQbB8Z6VnfEMD48e74sx+bC87zIriqmCXIiInoeAIgsU7yomNNo1vtHHpuBx69ohhzoc7g12KiJyEgiMIFn3UMr6REKfxjeMl9Yjhygn9eWXtPsoO1Qe7HBE5AQVHFyuvrWfd3mqmDcsMdikh6YaCgTQ0OeYuLQ52KSJyAgqOLvb+RwdxDs4ZruBoT15WMucOz+SJRTs5cqwx2OWISDsUHF3svS0HSU2MZUy/7nf/DW/ddf4wyg8f48kPdwW7FBFpR0CCw8xmm9kiM5vlS5u268wsxsyKzWyB52NMIOrtKs45Fm4t4+y8DKKjLNjlhKwJA9M4d3gmf3p3G7X1OuoQCTV+Dw4zuwKIds5NAXLMbJg3bU7wc2OBuc65Qs/HWn/X25U27z/EgUP1nDMsI9ilhLzvTB9O5ZEGfvf2lmCXIiJtBOKIoxB4zrM8H5jqZZv21hUAl5vZ+2b2tJnFtN2Qmc00s+VmtrysLLRvQbpwy0EADYx7YdyAVK6dNID/+2An6/dWB7scETlOIIIjCWidN6IG6ONlm/bWLQPOdc5NBaqAi9puyDn3qHMu3zmXn5kZ2m/I720tIy8rmZzUhGCXEhbuuXAkqQmx3P38GuoamoJdjoh4BCI4aoHWd8bkEzxGe23aW7fGOdc6Zeom4FPdXuGirqGJJTsqOEdHG15LTYzj11eNZcO+GmbNW6epSERCRCCCo4iPu6fGATu9bNPeujlmNs7MooHLgdUBqLdLLNlRwbHGZqYN1/iGLz4zsg93nT+MF4pKmL/5QLDLERHgU2MGfjAPWGhmOcAM4Bozu885N+skbQoA1866NcAzgAEvO+feDkC9XWLhljLiYqIoGKz5qXz17fOHsafyKC+uKKG5GT47KgsznZUmEix+Dw7nXI2ZFQLTgfudc6W0OVJop001QDvrqmk5syrszd98gEmD0jXNSCdERRn3f3Esu8oP887mA+yvqePK8f31uxQJkkAcceCcq+TjM6S8buPNz4Wjjw7Usr3sMDefNSjYpYSt6Cjj8jP7kdUrntfX7eOBf23h0rE5OOd09CHSxXTleBd4a8N+AKaPbu8EM/GWmTE1L4M7zs2jZ48YnllazMw5RZRW1wW7NJFuRcHRBd7aUMqYfik6DddP+qUlcEdhHhee1peFW8uY/pt3eWrxLpqbddaVSFdQcATYgUN1rNxdpaMNP4uOMs4Znskb3z6HsQNSmDVvHVc/+iHltZqOXSTQAjLGIR97fV0pzsHnTusb7FIi0sDeSTx162ReKCrhZ//cwJqSaq4c35/TNYmkBMim0hreXF/KnqqjAKQlxjGodyIFQ3oT1U3moNMRR4D9Y/VehvdJZkTfnsEuJWKZGVflD+DVb00jq2cPnllazPxNB3TBoPhVeW09dz6zggsfWMh7W8uorW/kaEMTq0uquO7xJVzx8CI2ldYEu8wuoSOOANpTdZRlOyv53gXDg11Kt9A/LZHbpw3hpZV7eHvjfmrrG7h0bE6wy5IIsLK4kjueWkHFkWN88zN5pCTEkhjX8vZ5rLGZhLgofvX6Zi578AN+fdVYPn9GvyBXHFg64gigf67eC7TcS1u6Rkx0FFdN6M+0vAwWb6/glbX7dOQhp+TDbeVc//gS4mKieOmOKXz3ghH/Dg2AuJgorp6Yy9vfOZczc1P51rOreHzh9iBWHHgKjgBxzjFv1V7G9U9hYO+kYJfTrZgZF57el7OH9mbRtnL++7VNCg/plKJdFXzlL0vpl5rAC3ecddKxs/SkOJ68dRIXjenLfa9s5OklkXsjMnVVBciakmo27qvhZ184PdildEtmxkVjsmlyjj+9t53EuBi+9dmwnSNTgmB7WS23PbGcvr3imTuzgIzkHh3+TI+YaB64+kzqGoqYNW8dfXvFc/6oyDujUsERIM8uKyYhNprPn6FuqmAxMy4Zm0O/1ER++/YWBmUkRnzfc6v9NXWs2l3F1v2HKK2po7auEQf0jI+hT8948rKSOSM3lewUXVvUnkN1Ddz2xHLMjL98ZZJXodEqLiaKP143nqv+tIhvP7uKv915NnlZyQGstuspOALgcH0jL6/ay8Vjs+kVHxvscrq1KDN+ecUYSiqPcPfza+iflsCEgenBLisgSiqP8GLRHl5Zu5ct+2v/vT41MZZe8bGYwaG6RioOH/v394ZkJjEwPZEJA9NJT4oLRtkhxznH955fza6KIzx922QGZfje1ZwQF82fbsznsgffZ+ac5cy78+yIei9QcATASyv3cPhYE9dOGhDsUoSW/wAfuWECVzy8iJlPFjHvzrMZkJ4Y7LL8puxQPW9v3M+seWtxwKRB6fy/i0YycVA6I/r2/MRALsDRY01sPXCIpTsqmL/pAAs2l7Fgcxmjsntx/qisbn8U8qf3tvPG+v3MungUBUM6P5t1v9QEHrp+PNc/voRvP7uKx27KJzpCrvNQcPhZU7Pj8YXbGTcglfG5acEuRzzSkuKYfXM+lz+0iFv+sowXvz4l7P8DrGto4p1NB/hg20Fio6O4/Zwh3HTWIPp1MLVNQlw0Y/unMrZ/KrdNG8LDC7axdEc5H24vZ8P8GvIHpnHxmGxSEsP799MZiz46yP2vb+LisdncOnXwKW9v8pDe/Nelo/nh39fzu7e38J0LRvihyuDTWVV+9ub6UnaVH2HmtCGatTXEDMlM5uEbxrPj4GHufHoFjU3NwS6p0zbuq+E3b23h/Y8OMj43je9eMIIfzBjVYWi0JyUhlumj+3L3BSOZlpfBiuJKPvvbd3lt7b6OfziC7K06yjfnrmRIZjL3XznWb6/fGwoGctWE/vx+/kf/nvA03Ck4/Mg5xyPvbiM3PZELT9cUI6FoytAMfn756SzcepAfvbw+7E7TbWxq5levb2LO4l30jI/hjsKhXDG+P8k9Tr3zICEumhljsrmjMI/M5B7c8fQK7pq7ktr6Rj9UHtrqG5v4+tMrqG9s5pEbJpDkh99nKzPjZ184nTH9UvjOX1exvay24x8KcQoOP3pjfSmrS6r5euHQiOnLjERXT8zljsKhPLOkmMcX7gh2OV47cKiOG2Yv4eEF25g4KI2vnTuU/mn+H6vpl5rA379xNt+ZPpx/rtnLZX94ny37D/n9cUKFc45Zf1vHqt1V/M9VYwNyBlR8bDSP3DiB2JgovjqniMNhHsYKDj9paGrm/tc3k5eVzBcn9A92OdKBuy8YwcVjs/nFaxvDoktm6Y4KLvn9+543t3FcfmZ/YqMD9/KNjY7irvOH8dRtk6k52sjn//AB81buCdjjBdPs93fwfFEJd50/jAtPzw7Y4/RLTeDBa89kW1ktd7+wOuyOdo+n4PCTOR/uYvvBw9xz4UhiAviCFv+IijL+96pxnDkglW//dRVLd1QEu6R2Oed49L1tXPvYYpJ6xPC3r5/dpf+YTBmawat3TWVM/xS+/ddV/Pjl9TSE8dhQW+9sPsAvXt3IjNP78u3zA3+B6Nl5Gdxz4UheXVvKI++G77Qkeofzg90VR/j1G5spHJHJZ0dlBbsc8VJ8bDSP3ZRP/7QEvvznpSzfGVrhUVPXwFfnFPGLVzdxweg+vPyNsxmV3avL68jqFc/Tt03mlrMH85dFO7n+8SWUHQr/+55sLj3EXc+sZGTfXvzvl8Z12ZToM88ZwiVjs/nV65t42TOfXbhRcJyipmbHPS+uITrK+MXlY3QmVZjpndyDubcX0LdXPDf/31KKdlUGuyQANuyt4dIH32f+pgP88JLRPHT9eHoG8fTh2OgofnTpaB64+gzWlFRxyYMLWVEcGr+rzthVfpgbZi8hIS6ax27O/9S1LoFkZvzPVeOYNCid7z23mne3lHXZY/uLguMU/c+bm1m0rZwfXTJat4YNU1meuYiyPOGxeHt50GpxzvH0kl1c/tAH1DU08ezMAm6dOjhk/iH5wpn9ePGOKcTFRHHNnxbz+MLtYXfL3h0HD3PdY0tobGrmqdsmd+oU5lPVerSbl5XM7U8uD7vwUHCcgheKSnh4wTaunZTLlybqKvFw1qdXPM/cPpk+vXpw4+wlPLd8d5fXcKCmjlv+sox7/7aOSYPTeeWuaeQPCr3pUU7LSeEf35jKOcMzue+Vjdwwewn7qo8GuyyvrC2p5qpHPuRoQxNzbp3M8D7Bu8FaSmIsT982mbzMZG57YhkvrSgJWi2+UnB00vPLd3P3C6s5O683P75sdLDLET/ITkngpa+fzaTB6Xz/hTXc/fzqLjltsrnZ8UJRCRc88B4fbi/nJ5edxhM+TqzX1VIT43jspgn88ooxrCyu4sIHFjJn8a6QvajSuZbf8RcfWUSPmCie++rJp0jvKmlJccydWUD+wHS+89xqfv7KBo41hubv8HgKDh81NjXzy1c3cvcLa5ial8HsmyfSIyY62GWJn6QkxPLEVybxzc/k8cKKEi747Xu8ub40YKdOLtxaxsUPvs/3nl/N4IwkXrlrGjdPGRQW9642M66dlMur35rGyL49+eG8dVz0+4Uh1+1yoKaOrz+9gu89v5ozBqTy8jdCa7balIRYnrhlEjcU5PLYwh1c+fAi1pZUB7usk9JcVT74cFs5P/nHejaVHuLGgoHMumSUQiMCxURH8d0LRnDu8Ex+8NJaZs4pYnxuKncU5vGZkVmnfHHnkWONvLq2lLlLiynaVcmA9AR+f+2ZXDImOywCo63BGUk8O7OAN9aX8otXN3Hz/y1lbP8UbigYyGXjcoiPDc5r5GBtPU8u2snj7++gsclxz4UjuX3a4JA8XT4uJor7vjCGs4dm8MO/r+fzf3yfL5zZjzvPy2NoZuiEXCsFRwcqDh/jrQ2lPLtsNyuLq+iXmsAjN0zQlCLdQP6gdF791jSeX17CH+Zv5fYnW27qM2NMX84emsG4Aalk9uy4O8k5x67yIyzZUc7i7RW8vWE/h+obGZKRxH9dOprrJueG/T8gLXddzOa8kVk8t2w3cxbv4vsvrOHnr2zk/JFZTBuewdS8TK9+X6ei7FA9y3ZW8Orafby5YT/HGpu5eEw2d39uRKemR+9qM8ZkMyUvgz/M38qcxbt4acUezhrSu+VvLi+DIRlJIXGiRECCw8xmA6OAV51z93nbxtt1/lbX0ERxxREO1NRTVlvHvuo6NuytYd2eanaWHwFa/qv6yWWn8aX8ASTEhfeLXLwXGx3FdZNz+VJ+f97asJ+XVu7h6SXF/PmDnUDL1cC56Yn0To6jd1IcMdFRNDY109DsKK+tp6TyKLsrjlBT1zJW0jspjumn9eGaiblMHJQWEm8C/tQjJpobzxrEDQUDWbqjgmeX7WbBljJe8lx13i81gWF9khmWlUy/1AQyevYgM7kHPeNjiY+NIj42mvjYaHrEROFoOd3dOUdTs6PZtXxdW9/IoboGauoa2Vd1lD1VRymuOMKq3VXs8rxe05PiuGbiAG46a1BIdUt5IyUhlnsvHs3Mc4by12XFPF9Uwo/+vh6A7JR4TstJYXBGIoMykujbK57UxFhSE+PISOrRZTMa+z04zOwKINo5N8XMHjKzYc65rR21AcZ4s67ttvxhw74arnho0SfW9UtNYEy/FK7KH8C5wzM5LadXxL3IxXsx0VHMGJPNjDHZHD3WxLq91azeXcXqkmpKq4+yYW8NB2vraWp2xERHERttpCfF0T8tkQkD0xjepycFQ9IZmpncLf6OzIzJQ3ozeUhvmpsdG/bVsHDrQTbuq2HrgVoWbSv32yBwlLWc2HBaTi+un5zLhIFpjOufGpJdUr7I7NmDb3xmGHeel0dxxRE++KicRdsOsnV/LQu3llHf5vd30Zi+PHT9hC6pzfw96Gdmvwded869amZfBHo65/7cURvgTG/WtbOtmcBMz5cjgM1+3SHfZQAHg1xDV9L+Rjbtb+Q6fl8HOucyvf3BQHRVJQGts6HVAHletvF23Sc45x4FHvVH4f5gZsudc/nBrqOraH8jm/Y3cp3KvgbiWK4WaL0UM/kEj9FeG2/XiYhIEAXijbgImOpZHgfs9LKNt+tERCSIAtFVNQ9YaGY5wAzgGjO7zzk36yRtCgDn5bpQFzLdZl1E+xvZtL+Rq9P76vfBcQAzSwOmA+8550q9bePtOhERCZ6ABIeIiEQuDTaLiIhPFBx+ZGazzWyRmc3quHX4MbMYMys2swWejzFm9hMzW2Zmfwh2ff5kZn3MbKFnOdbM/ul5bm850bpw1mZ/+5lZyXHPc6Znfdj/fZtZipm9ZmZvmdnfzCyuvf2KhH2FE+7vJ17DnnY+vY4VHH5y/NXwQI7nyvdIMxaY65wrdM4VAj1oOettElBiZp8NZnH+4hlXe4KW64gAvgks9zy3l5hZzxOsC0vt7O9k4Oetz7NzriyC/r6vB37jnJsOlALX0Ga/Imhf4dP7+58c9xp2zq01s3x8fB0rOPynEHjOszyfj08jjiQFwOVm9r6ZPQ18BnjRtQyUvQ1MC2p1/tMEXE3LRafwyed2EZB/gnXhqu3+FgBfN7MPzey3nnWFRMDft3PuIefcW54vM4Eb+PR+FbazLiy1s7+NHPcaNrMY4Bx8fB0rOPyn7VXufYJYS6AsA851zk0Fqmi5ODPi9tk5V+OcO/6GCO09txHzfLezv68BU5xzZwHDzWwsEbS/AGZ2FpAG7CaCn9tWx+3vW3zyNXwRndhfBYf/dIer3Nc45/Z5ljfRPfYZut+sBoucc4c8y5uAYUTQ/ppZOvAgcAvd4Llts79tX8Odem7D+hcSYrrDVe5zzGycmUUDl9Pyn0qk7zN0v1kN3jCzbDNLBD4HrCNC9tfM4mjphvqBc24XEf7ctrO/bV/Dq+nE/upGTv4zj/C7yt1XPwWeAQx4GbiPln3+HXCh5yMSPQG8ambTgNHAEloO7duuixQ/Ad4BjgGPOOc2m9k+IuPv+1ZgAnCvmd0L/Bm4MQJmrDiRtvv7DjAHz2vYOfe2mUUBv/TldawLAP2oO17lbmYJwMXACufc9mDXEyieN5GpwBut4wHtrYtkkfr3rRkrfH8dKzhERMQnGuMQERGfKDhERMQnCg4REfGJgkPEj8zsL2a2ysyWm9ntnq9XHjc3UF8z22lmd3ral5rZl4NctohPdDquiP99A9hIyznyS4FvOufeb/2mmQGMNbM+RMBVydL96IhDJACcc+XAK7TMA9RWMTCYloutNnZlXSL+oCMOkcApB1KBB82sGihzzl0FNNMyseCZtFy1KxJWFBwigZMOVNCmq8pjK3AF8GyXVyVyitRVJRIAZpZKy3QV80/QZAWQBVR2VU0i/qIjDhH/exCoB+6hZd6f1q4qgP/yfC6iJTxEwo6mHBEREZ+oq0pERHyi4BAREZ8oOERExCcKDhER8YmCQ0REfKLgEBERn/x/esIhmZWmsugAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Slope', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Solar radiation', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Next_Tmax', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Next_Tmin', ylabel='Density'>"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制每列数据的直方图（分布频次）\n",
    "plot = data.drop('Date',axis=1) # 删除'Date'列\n",
    "\n",
    "for I in plot.columns:\n",
    "#     plt.figure(figsize=(10,8))\n",
    "    sns.distplot(plot[I]) \n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc578456",
   "metadata": {},
   "source": [
    "**生成数据概况报告**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "id": "e112e3ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成整体数据概要\n",
    "# data_report=pp.ProfileReport(data) \n",
    "# data_report\n",
    "\n",
    "# 也可以把生成的报告导出保存\n",
    "# data_report.to_file('D:/数据分析/第八阶段/模块三/作业/作业report.html') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17af23f1",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 725,
   "id": "5acb5811",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 由于本数据集的特殊性（有两个样本标签：次日最高气温和最低气温）\n",
    "#，本案例中我们只进行次日最高温预测--因此这里只选出与最高温预测相关的变量，作为建模要用的数据\n",
    "\n",
    "Max = data[['Present_Tmax','LDAPS_RHmax','LDAPS_Tmax_lapse','LDAPS_WS','LDAPS_LH','LDAPS_CC1','LDAPS_CC2','LDAPS_CC3','LDAPS_CC4','LDAPS_PPT1','LDAPS_PPT4',\n",
    "          'lat','lon','DEM','Slope','Solar radiation','Next_Tmax']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 726,
   "id": "b766e393",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.7</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>0.233947</td>\n",
       "      <td>0.203896</td>\n",
       "      <td>0.161697</td>\n",
       "      <td>0.130928</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.9</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>0.225508</td>\n",
       "      <td>0.251771</td>\n",
       "      <td>0.159444</td>\n",
       "      <td>0.127727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.6</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>0.209344</td>\n",
       "      <td>0.257469</td>\n",
       "      <td>0.204091</td>\n",
       "      <td>0.142125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>0.216372</td>\n",
       "      <td>0.226002</td>\n",
       "      <td>0.161157</td>\n",
       "      <td>0.134249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31.4</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>0.151407</td>\n",
       "      <td>0.249995</td>\n",
       "      <td>0.178892</td>\n",
       "      <td>0.170021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS    LDAPS_LH  \\\n",
       "0          28.7    91.116364         28.074101  6.818887   69.451805   \n",
       "1          31.9    90.604721         29.850689  5.691890   51.937448   \n",
       "2          31.6    83.973587         30.091292  6.138224   20.573050   \n",
       "3          32.0    96.483688         29.704629  5.650050   65.727144   \n",
       "4          31.4    90.155128         29.113934  5.735004  107.965535   \n",
       "\n",
       "   LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0   0.233947   0.203896   0.161697   0.130928         0.0         0.0   \n",
       "1   0.225508   0.251771   0.159444   0.127727         0.0         0.0   \n",
       "2   0.209344   0.257469   0.204091   0.142125         0.0         0.0   \n",
       "3   0.216372   0.226002   0.161157   0.134249         0.0         0.0   \n",
       "4   0.151407   0.249995   0.178892   0.170021         0.0         0.0   \n",
       "\n",
       "       lat      lon       DEM   Slope  Solar radiation  Next_Tmax  \n",
       "0  37.6046  126.991  212.3350  2.7850      5992.895996       29.1  \n",
       "1  37.6046  127.032   44.7624  0.5141      5869.312500       30.5  \n",
       "2  37.5776  127.058   33.3068  0.2661      5863.555664       31.1  \n",
       "3  37.6450  127.022   45.7160  2.5348      5856.964844       31.7  \n",
       "4  37.5507  127.135   35.0380  0.5055      5859.552246       31.2  "
      ]
     },
     "execution_count": 726,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 727,
   "id": "6d9a5dcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 17 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Present_Tmax      7682 non-null   float64\n",
      " 1   LDAPS_RHmax       7677 non-null   float64\n",
      " 2   LDAPS_Tmax_lapse  7677 non-null   float64\n",
      " 3   LDAPS_WS          7677 non-null   float64\n",
      " 4   LDAPS_LH          7677 non-null   float64\n",
      " 5   LDAPS_CC1         7677 non-null   float64\n",
      " 6   LDAPS_CC2         7677 non-null   float64\n",
      " 7   LDAPS_CC3         7677 non-null   float64\n",
      " 8   LDAPS_CC4         7677 non-null   float64\n",
      " 9   LDAPS_PPT1        7677 non-null   float64\n",
      " 10  LDAPS_PPT4        7677 non-null   float64\n",
      " 11  lat               7752 non-null   float64\n",
      " 12  lon               7752 non-null   float64\n",
      " 13  DEM               7752 non-null   float64\n",
      " 14  Slope             7752 non-null   float64\n",
      " 15  Solar radiation   7752 non-null   float64\n",
      " 16  Next_Tmax         7725 non-null   float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "Max.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 728,
   "id": "79e47bbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Present_Tmax</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>100.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>21.857621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.356080</td>\n",
       "      <td>0.258061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.312421</td>\n",
       "      <td>0.558694</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.318404</td>\n",
       "      <td>0.250362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101388</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.496703</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.254348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081532</td>\n",
       "      <td>0.227664</td>\n",
       "      <td>0.499489</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.591995</td>\n",
       "      <td>1.945768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052525</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>16.655469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solar radiation</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmax</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "Present_Tmax      7682.0    29.768211    2.969999    20.000000    27.800000   \n",
       "LDAPS_RHmax       7677.0    88.374804    7.192004    58.936283    84.222862   \n",
       "LDAPS_Tmax_lapse  7677.0    29.613447    2.947191    17.624954    27.673499   \n",
       "LDAPS_WS          7677.0     7.097875    2.183836     2.882580     5.678705   \n",
       "LDAPS_LH          7677.0    62.505019   33.730589   -13.603212    37.266753   \n",
       "LDAPS_CC1         7677.0     0.368774    0.262458     0.000000     0.146654   \n",
       "LDAPS_CC2         7677.0     0.356080    0.258061     0.000000     0.140615   \n",
       "LDAPS_CC3         7677.0     0.318404    0.250362     0.000000     0.101388   \n",
       "LDAPS_CC4         7677.0     0.299191    0.254348     0.000000     0.081532   \n",
       "LDAPS_PPT1        7677.0     0.591995    1.945768     0.000000     0.000000   \n",
       "LDAPS_PPT4        7677.0     0.269407    1.206214     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7725.0    30.274887    3.128010    17.400000    28.200000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "LDAPS_RHmax         89.793480    93.743629   100.000153  \n",
       "LDAPS_Tmax_lapse    29.703426    31.710450    38.542255  \n",
       "LDAPS_WS             6.547470     8.032276    21.857621  \n",
       "LDAPS_LH            56.865482    84.223616   213.414006  \n",
       "LDAPS_CC1            0.315697     0.575489     0.967277  \n",
       "LDAPS_CC2            0.312421     0.558694     0.968353  \n",
       "LDAPS_CC3            0.262555     0.496703     0.983789  \n",
       "LDAPS_CC4            0.227664     0.499489     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.052525    23.701544  \n",
       "LDAPS_PPT4           0.000000     0.000041    16.655469  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  "
      ]
     },
     "execution_count": 728,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4177e1f1",
   "metadata": {},
   "source": [
    "**填充缺失值**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 729,
   "id": "76abc816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Present_Tmax        70\n",
       "LDAPS_RHmax         75\n",
       "LDAPS_Tmax_lapse    75\n",
       "LDAPS_WS            75\n",
       "LDAPS_LH            75\n",
       "LDAPS_CC1           75\n",
       "LDAPS_CC2           75\n",
       "LDAPS_CC3           75\n",
       "LDAPS_CC4           75\n",
       "LDAPS_PPT1          75\n",
       "LDAPS_PPT4          75\n",
       "lat                  0\n",
       "lon                  0\n",
       "DEM                  0\n",
       "Slope                0\n",
       "Solar radiation      0\n",
       "Next_Tmax           27\n",
       "dtype: int64"
      ]
     },
     "execution_count": 729,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用每列的均值填充其缺失值\n",
    "Max.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 730,
   "id": "2ebfed2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in Max.columns:\n",
    "    if Max[i].isnull().sum()>0:\n",
    "        Max[i]=Max[i].fillna(Max[i].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 731,
   "id": "cc2f8ccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Present_Tmax        0\n",
       "LDAPS_RHmax         0\n",
       "LDAPS_Tmax_lapse    0\n",
       "LDAPS_WS            0\n",
       "LDAPS_LH            0\n",
       "LDAPS_CC1           0\n",
       "LDAPS_CC2           0\n",
       "LDAPS_CC3           0\n",
       "LDAPS_CC4           0\n",
       "LDAPS_PPT1          0\n",
       "LDAPS_PPT4          0\n",
       "lat                 0\n",
       "lon                 0\n",
       "DEM                 0\n",
       "Slope               0\n",
       "Solar radiation     0\n",
       "Next_Tmax           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 731,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "867e1845",
   "metadata": {},
   "source": [
    "**标准化处理连续型特征**       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 732,
   "id": "94b2a4b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 733,
   "id": "e3199651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "      <td>-0.376282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "      <td>0.072097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "      <td>0.264260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "      <td>0.456422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "      <td>0.296287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "      <td>-0.632499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "      <td>-0.536418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "      <td>-0.792634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>-3.304127</td>\n",
       "      <td>-4.113443</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>-1.939757</td>\n",
       "      <td>-2.267499</td>\n",
       "      <td>-1.412018</td>\n",
       "      <td>-1.386643</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845455</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "      <td>2.762374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7752 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7750     -3.304127    -4.113443         -4.087857 -1.939757 -2.267499   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7750  -1.412018  -1.386643  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  -0.376282  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950   0.072097  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534   0.264260  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176   0.456422  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205   0.296287  \n",
       "...        ...       ...       ...       ...              ...        ...  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  -0.632499  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  -0.536418  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  -0.792634  \n",
       "7750 -1.758184 -2.082302 -0.911963 -0.845455        -2.358212  -4.123453  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935   2.762374  \n",
       "\n",
       "[7752 rows x 17 columns]"
      ]
     },
     "execution_count": 733,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#标准化处理连续型特征 \n",
    "Max_scal= StandardScaler().fit_transform(Max)\n",
    "Max_scal = pd.DataFrame(Max_scal,columns=Max.columns)\n",
    "Max_scal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "838bf241",
   "metadata": {},
   "source": [
    "**划分特征列和标签列**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 734,
   "id": "869f30c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  LDAPS_CC1  \\\n",
       "0     -0.361326     0.383078         -0.524889 -0.128382  0.206966  -0.516243   \n",
       "1      0.721084     0.311586          0.080895 -0.646994 -0.314841  -0.548557   \n",
       "2      0.619608    -0.614982          0.162936 -0.441604 -1.249283  -0.610450   \n",
       "3      0.754909     1.133054          0.031092 -0.666247  0.095997  -0.583539   \n",
       "4      0.551957     0.248765         -0.170325 -0.627154  1.354409  -0.832287   \n",
       "\n",
       "   LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4       lat  \\\n",
       "0  -0.592636  -0.629013  -0.664815    -0.30575   -0.224453  1.189286   \n",
       "1  -0.406199  -0.638055  -0.677462    -0.30575   -0.224453  1.189286   \n",
       "2  -0.384009  -0.458843  -0.620575    -0.30575   -0.224453  0.653021   \n",
       "3  -0.506548  -0.631178  -0.651696    -0.30575   -0.224453  1.991696   \n",
       "4  -0.413115  -0.559990  -0.510358    -0.30575   -0.224453  0.118743   \n",
       "\n",
       "        lon       DEM     Slope  Solar radiation  \n",
       "0 -0.005000  2.772243  1.115004         1.517935  \n",
       "1  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3  0.385280 -0.297588  0.932424         1.201176  \n",
       "4  1.807917 -0.494322 -0.548433         1.207205  "
      ]
     },
     "execution_count": 734,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Max_scal.drop(['Next_Tmax'],axis=1) \n",
    "X.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 735,
   "id": "72f62715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7750   -4.123453\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7752, dtype: float64"
      ]
     },
     "execution_count": 735,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = Max_scal['Next_Tmax']\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7931cf04",
   "metadata": {},
   "source": [
    "# 创建线性回归模型并评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 736,
   "id": "af227247",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "#MSE\n",
    "from sklearn.metrics import mean_squared_error\n",
    "#RMSE\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16d29c33",
   "metadata": {},
   "source": [
    "**划分数据集**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 737,
   "id": "ce72dc45",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2,random_state=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 738,
   "id": "89c8ebb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6201, 16), (1551, 16), (6201,), (1551,))"
      ]
     },
     "execution_count": 738,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,X_test.shape,Y_train.shape,Y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b36ce43e",
   "metadata": {},
   "source": [
    "**建模**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 739,
   "id": "d73b44e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 740,
   "id": "f7f532ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model.fit(X_train,Y_train)\n",
    "Y_predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "588cbb4c",
   "metadata": {},
   "source": [
    "**评估**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 741,
   "id": "3405ccf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03946390012950553"
      ]
     },
     "execution_count": 741,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 742,
   "id": "9778ac6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005204486032274113"
      ]
     },
     "execution_count": 742,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 743,
   "id": "b84428e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7548779106664724"
      ]
     },
     "execution_count": 743,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 744,
   "id": "d6d7a636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7517624012417917"
      ]
     },
     "execution_count": 744,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63dc0f1d",
   "metadata": {},
   "source": [
    "**计算MAE，RMSE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "id": "df57b705",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.23443534187388365\n",
      "RMSE: 0.4962357527761606\n"
     ]
    }
   ],
   "source": [
    "MAE = mean_squared_error(Y_test, Y_predict)\n",
    "RMSE = np.sqrt(mean_squared_error(Y_test,Y_predict))\n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE)  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026fa438",
   "metadata": {},
   "source": [
    "# 模型诊断"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09eba51e",
   "metadata": {},
   "source": [
    "## 绘制线性拟合图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 746,
   "id": "7a96253d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "execution_count": 746,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x243d6bd4850>]"
      ]
     },
     "execution_count": 746,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x243d6bd4be0>]"
      ]
     },
     "execution_count": 746,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x243dd8d0a90>"
      ]
     },
     "execution_count": 746,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画个折线图来看看模型拟合的好坏程度\n",
    "plt.figure(figsize=(20,10)) \n",
    "plt.plot(range(len(Y_test)), Y_test, 'r', label='Y-TEST')\n",
    "plt.plot(range(len(Y_test)), Y_predict, 'b', label='Y-PREDICTED')\n",
    "plt.legend() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 747,
   "id": "dd9d5d55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "execution_count": 747,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2438627ef70>"
      ]
     },
     "execution_count": 747,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24392cc9880>]"
      ]
     },
     "execution_count": 747,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YmhGmkdTAYbzywLX69rh5t+Dk+kvLWuwGAdNzhBBC8kope/A4pQ2tjCGDwOrcZUaXA8l01j4n40dZBCksRFYkTEWmcDqyayN6f/2Q/vgZn/sZwmNOcvW+IxmKJkIIITlTqh48bpy9vYwu8YLVudulyqyQic72jjgGjg9m7TdHmIyoAE6vFtix/DJ9X837FiP6T9e7ft+RzsgqiSeEEJIXvHrwtHfEMbtlC85a9mSWV1GQ2BVAa+QqEJSQgBLOdKrUzt18nnOmjbe8TlELd3DAWnhpQtBczxSNKLrHkxXi5R0ZgumhTdsx/fKbXL8vYaSJEEJIAHjx4Cnk4FY3aUOv0R8jYSGw+ILJaJwyLuvcAbieB7dz/yE8sr076/hnnhrJGMKrFXhbRZPGjq7Sr5/xfdV0Cge/exMG+14HAFSf+88Yv6AZa7b3YtH5McuhvuVkOFlIKJoIIYQEgtNoDo1CDm51kzZsnjfVslPugjNrsf3FXqRsGqZSqor1u+JonDIuq+h7dssW1/PgjJEvI8/sO6TXIWniUpZ+04SgUcC++Kc/4LW1zfpzJn7smxg14eyMtaxaOKPoVhHlYldB0UQIIaSgFLJo3I1TNoCs1rJUSsWzL2cKJqsONEAu+Lycp+y55vdLJFMIC2Ep5IxC8MP1k/Dgshvx2tNPAwBGTZyK06//OoRp4N3BvoRrsZsvChl5zBWKJkIIIQWlkEXjbtKGrZv3Zo1ESQNIpzL32Rn0yM7H7XnWRBTp4F8zssjXnGnjAQB79+7FtGnT9P0TFt+NyJn1lq8phdqlQkYec4WiiRBCSEGxiv4IDAmP2S1b9CiQn3SNLM1j99qgIlztHfGM93Ed5QJgCgD5YuueHnz2s5/Fd77zHQBAVeRkTPrsjyHC1kXmpVK7VMp2FWYomgghhBQU81gRY9or3pdA87rdgAo9+uOUrtGEktWx3KR5cikEN6K9FzAs+KLVCkZXhXA4kcwoEDcXd/dZOHt7IfV2L55ZPR/PnNj+yU9+gi/9qVYaHYuVUN1QqdpVWEFHcEIIIUVD5iRuhZXDttW4EzevyzrG47szUnQhAOGwsD2uFdGIgmODaamzt2zczBglZDkSBZDXUmkc3rEeff/9w+Htw4dxyimnlI1Le6mN4LFzBKdPEyGEkKLhJQWjpe/aO+K6/9GStk5HYePqPUzpsfCJOXMyzyMZfYmkrS+UrH5HVZHl36QhO7v0sQHsXz1fF0zX3bIMqqrilFNOAeDdO6tYNDXEdH8pgSFRV6pjXJieI4QQUjS8psas0ndO1EiMIzVaN+/NEl7JlKpbA9jNeHOLJtxkAu5wIok1i+uxpK3T1fH6//zfeHPj1/XtM2/5MRbdmBk98uKdVWyK3cHnFoomQgghRcOqWFoJC6RSKrKnsQ3hNWXmVGQtE23afi/CbuyosOWgX60+J1qtWKbhotUKmhpiWLmxyzZNl04N4pXv/B+kB/oAACfNvBSnXnoLVMCy26xcxEi5wPQcIYQQAIUbbWLEKjWz+L2TEQ4H0E52Aqci67BEVWn7rdJcMpRwyDYlJisj1vbblRkn9v8R3V9v0gXTpI8/iFMvvUV/vBS7zSoNRpoIIYQU1WDQHA2Z3bLFczTJDqcuLJnvkbbf3O1nh5Zmk6XEDku8mLT9Vo+rqorX196OY/EXAABjpszEhMV3ZxlVlmK3WaVB0UQIIaSkDAadIiZKWLiuaYooYcyZNj6rxd94TjFJ+s1YBK4Ju3PufMp2rMqkaMQ2JebUXm9+/PgbL+HVH96qb7/joy0YM/kfEVHCnBdXBJieI4QQUlIGg3YRk1g0gtYrZ6L1qpmOnW2xaEQfSBvvS0DFcATNmHrUnLTNWO2/ZtZk6fvZCRct9al5ScleZ0wFvvnkfbpgCp8yAXXNv8CYyf8IAGXTbVZpMNJECCGkJAwGZSaVgLVvj+Z7ZDVst/XKmWhqiEmH5hojaFv39Fiu59EdB9A4ZVzGe97dNEN/zBhxikYUCAEsbetE6+a9GdEsc+pTxbD3ktlksqkhhi27/owHPvkh/dinfXgZxk77gL5de6JoPCiRlOuw3HIZthsEFE2EEEI8jfzIB16EhRGntno3ETTZc1KqiqVtnVjS1qmLor6BIWfve6+eKRVF5nowq9Sndl5mk8mP3Px5tD28Rt+evHQdQqPG6NtKWGD5gumW6/VDrrVs5TRsNwgomgghhKCpIYad+w/pEZSwEFh0fuHa1e2ERfO8qWjdvBdL2zoRrVagqsgYS5JLDREgtwHQ1gAgY5huvC+BJSfEVCwawcDxQdtolhvh1tfXh9raWn27du4nccp7P5zx/NoT526MZgG5+TDlWstWSrVwhYCiiRBCSpRCpj3aO+JYvyuup5xSqor1u+JZ6al8IRMW8b5ExogTo7hxE9VwE0HLZZqYXTeddk41ESVDdOnvi6FOwYbEH/DtrzTr+8+4dS3C1TVZzz+aTGdEdJof3400gJRhRl/zut0A3Ed5cq1lK6VauEJA0UQIISVIodMexY4YyCJCAvZdcolkCis3dknFpV36ThOlVoImqHMC5Oaa6mAS//ulRXhm8BgAYOnSpdgxYb7ldQgLkfX5WF2XZErFyo1drj+zXGvZSqEWrpCwe44QQkoQOxGTD4odMbAykHQaVKvRO5DM6I5b2taJMw0GnU0NMWxbNhcvtVyGbcvmDheQP77b0wgXLwgMz8qzSv0l9j2H7nuvgHpCME279Qe47777pPPi7GwOzMhSjVbkOp+uXObbBQVFEyGElCCFFjGyyEChIgZGZ3DAvWCyQnudlb2AxooNXa5n17khGlEs1262GFDVNA7+4Ba8sW4lACDyrgsx5Y5NSFRPwOyWLQCs7QS8Dg52S67Dcstp2G4QCDWXZK4LGhsb1Z07d+b1PQghpNLQPH3MGAujg2wRB2BZ+1OMG6Ds3P1SW62gelRVxvm6HYzrlusurMPdTTOkaxcAjr76V7z246X6vtOvvxejJ2VGZGTX3JyutSMaUdC5/BJ/J+LASLAXEELsUlW10fIxiiZCCCk9rG6SESWsmzWab5611QqWL5jueAOTHXfVwiH/oVK4IZ617ElXUaZYNILe/mMYSMpG+1qjhESgUSZg6Pp3fPkS6dp7fv41DPzlmaH3P20KJt74AISwTvZEIwrGjq6S1mBp++dMG4+2Zw9knIsSEmi9amZePje7704lCSc70cRCcEIIyRO5/CqXFTBb1ToBQ3UsbgrF7WqltHofv8jO1+t1kBUXa4QA3Le4Hk0NMdSvfNqzaApaMAHDdUTmtSf7XsPBhz6hb49f9GVUv/MC22P1JZJ6cbq5AcB83RqnjCuY0C12s0Ap4Fs0CSHeAWCdqqofDHA9hBBSEQTR/WZ1k1xqk1ZycwPLV62UVlidNLa/P74bO/cfyoiM+bUJMBIOD1cJ5dL5Fg4JvV0/KIxr7936Axx59gkAQJUyCm8dOYyL7n/Gc+rR7nMN0hnciWI3C5QCvgrBhRC1AH4EYGywyyGEkMogX91vToXZTjewfBV8WxVWJ9Mq1u7o9nwdzEXhZpIpVX99WNbP7wKZYPJTdB1Rhm6nTQ0x3Dknhv2r5+uC6dN3rUby+DGMGTPGstvMDaUgTIrdLFAK+O2eSwFYDOBIgGshhJCKIV+/yp1uuk43sHy1iMsiPrKyWafroNkEyCSR1s7vpRXfDZr48cqYE9f0W9/6Fv7tovP0/YcOHcJ3vnq7vm3uNotGFNRWK3rnWW21Ynn8UhAmI81ewApf6TlVVY8AgJAofCHETQBuAoC6ujq/ayOEkLJDq9+R3cpzvflpqZgVG7qyhIqbG5jTrLZCYbwOdjVPdvVN+fBYSiTTvo576PBbGffEu+66C1/96leHzu2hDsT7EggLgZSq2s7SkxVbl4IwKZXvTjHJqXtOCPHfqqr+i91z2D1HCBkpOLWFB91pVErt3w1fedq1qaISFmi9cqZeJG7XkeWl1b5YDOx9Bj3tX9O39+/fj7q6uqE6r3W7kUxl32ftvgul9LmORPJmOUDRRAghw9j5C9lFF4yU6w3TTiBYoVkktG7eK/U1AoZMIgWA6lFh9B+XC6dczDC9or2Xmk7h4MM3YfDw6wCAj370o1i7dq3+PCchGYtGsG3Z3DyvlniFlgOEEBIQdqJGVqcjAFc3x0LPm8uFu9qfx6M7DiClqggLgWtmTUbrlTOlIsiMZpEgiyCppv+2E0zac6IRBf3HBvNiKaChib0vPbQef3roc/r+2McewFmX/3PGc50ib6VQ3E28kdMYFacoEyGEVBKaqDHOOTOO6ci1u6jQ8+b8clf783hke7dehJ1SVTyyvRs79x/CtmVzXXefBZ1y60skIa0cd0ArzHYinVbx4LIbdcE0atJU1N2+EVUTzsIj27txV/vzrt8zKin6JqULZ88RQohLnESNrLtozrTxmN2yBWcZhshaUciC51x4dMcBy/1rt3cHPgLFK27Tg0bCQmDbsrmO6b3k31/BH1deiqeffhoAMGHx3Zh4/b0ZBeA/3dGt/3c0Yi+K8jyQI4v2jrir7yGRw/QcIYS4xMlGwKq7aM608a7NHbXuKjO5eBHlilU6Utbmr0Xfyg3tfGqrFWlK7e9PfwdvdzwFAAiNORln3PJjiHC2KEqrQ7VtzfOmYsXl0zMMP80czsGY0yvllPotZRhpIoQQl7hJv2n+Qi+1XIZty+Zi654e1yk3mRgJ2ovILVbpSDtH8mITykFbnnPnU3j7aLaISb3di/2r5+uC6dT5t2Hy5x61FEwaRkHSetVMqegtpPdSuaR+Sx2KJkIIcYkfcz8vJpeyWiA/DtVBYHWjLY58c8foqpAvt21gSJiaR9gd3rEer3z7en178pLHcNL0Oa6OZxx9cu/VM4tuCskRKMFA0UQIIS4xuznHohFH3yUvxeGl5rhcbjfURDJtO35Fo9rB9Tt9bAD7V89H33//EAAQ/acbMOWOTQiNrva0HmPa1uv3Jmg4AiUYWNNECCEe8Dog1Wr4rEwIlZrjsp0btxciSgjHBtNIq0P1WReeXYs/dB8OvHsuLIT++dSvfNpytEu1EkLt2NEYkJxX/5//G29u/Lq+Hfvsj1F10jhf61ExXN9UyMG6Vnj5HhI5FE2EEJJHvAqhQtxc3RpoWt1o/TBu7Ois99DWEGThuLH2K5lKWz5nIJm2FExqahCvfOffkB44DAA4aealOPXSWxzfM2ZR7G+kVAquS02QlysUTYSQEU++XbiLHWUw4qaLyng9otUKRleF0JdI+nbdtnoP7Zponk9BYEzLOZlhGkns3403fvZFfXvSxx+Ectpk29cYR8EAQOOUcVIRaKxvKjTl6jBfqlA0EUJGNOXYip3LjdCui8pq1lvvQBIRJYz7F9frr/cTHZIJh617ehxfG1HCjtEuP6kmVVXx+trbcSz+AgCg+sx6nHb1V+XD6DEkGmurFagqsLStE62b92ak385a9qSlsHRTHxa0wCnH73apw0JwQohvKsEsr9xasZ1cyZ1w6qJyElXbls31a7pt+d5uxISb4m5zYbWTseTxN15C9z0LdMH029/+FhMW3y0VTLFoBGsW1+P+xfU4mkyjL5HMuP7Xfvd/cc6dT0kjcU4F17l+rlaU23e7HKBoIoT4Ih9/5ItBubVi53ojrJGICW2/3fXQRLJf2wGjcHB7LIFh7ys7jKnF2S1bLIvANd588j68+sNbAQDhUybgH764CR/84AelwkYbrNvUEJNe/237Dkn9tNxEwfIhcMrtu10OMD1HCPGFU0SiXJB1iJVqK3auN0KZubi2X3Y9aiKKp6JwJSwyRpooYYEzT43gnDuf8mTWee2Fda6e1/CVp9E7YF93NXikB/EHP6Zvn/bhZRg77QMYpQwJRjcdZl4FR8xlmi0fAqfcvtvlACNNhBBfVMqv2FLzRnIiV7+dPsmYEG2/7HoI4X7Abm21kqVckinVNhpjJiwErruwDnc3zcg8rgRt/Ins6H2/X5shmCYvXYex0z4w9FgiqdcTJZIp3cG79kQR/NK2Tj39nC/BkQ8fpXL7bpcDjDQRQnxRKb9iS60V26kY2E00xO4Ydp+bWTikVFWPlLgdnxJRwlBVSOetOfFyy2XSxy47b6LnTrv00bdx4Bsf0bdrL/okTmn8cNbzlrZ16oIrpapQwgJvHx3Uz0NLPy86Pya1F7BCe93O/YewdU9P1mditF4wR8m8Chyrz33Vwhkl892uBISa55lGjY2N6s6dO/P6HoSQwmPuzAGG/sgX2um4knB7Te1EkdMxZI9biQHj62a3bHHsmlNCwElj5ENv3WCXzpIZVsp4a/fTOPSrb+rbZ9y6FuHqGt9r09Y3Z9p4PLrjAFKqKh2ybMZKEFldc+15btN6Gvz3GBxCiF2qqjZaPkbRRAjxCz1ggkUmTLRC5KCOYfW5yawEtNdZ3ZTzhflm79UIUx1M4sA3FkMdPA4AOLnxwxh30ScDXV8Q10EmuLx83hpBfHfIEHaiiek5QohvSsm0sRIIok7MzTGsPjdZ+i3el8Dsli1ZRpf5xM43yvG1+57DG+tW6tuTbvoulNqJga8vCGQRKj8+WJVSY1jqUDQRQkiJEESdWLTaOj0WtSmitntvYPgmrhldFoJ4X0LviHODqqbx6g//HcmelwEAkXddiAkL78rjCvNHWNbiaEOl1BiWOhRNhBBSIngdqmqVZpNVXBj3W73O7Zy5QqTnNNwKpmOv/hWv/Xipvn36Dfdh9MR3+37fWDSCgeODnmuzqpUQEsk0oiccw+1Gz9il+LxYMmjk2iBA3EHLAUIIKRGaGmK6+7XA0M1bVsgrMxeVpc4On9gvex0w5KrtJ8pRTHp+/jVdMCmnTUHd7RtyEkwAsG3ZXFx2nveU3kAyjWsvrEPHly9B5/JLEItGLAVTWAhbl3Mn93MrnL47lWJGW2wYaSKEkBLCbZ2YzFxUVlyspWnsTEm3LZvrylpg7Kiwp4G4+SDZ9xoOPvQJfXv8oi+j+p0XBHLs2S1b0H9s0NdrH91xQPeWktUTpVVV/4y9RBadsPvuVIoZbbGhaCKElA1MLwwjuyGnVDUr9WO8ETsVDNdEFNtCbwGg/3gKo6tCODaY9rn63Ojd+gMcefaJoY2wgrolbRBVowI7vp9CbA2jYHWqMyqkRxgLxYOBookQUjJ48R8a6RPbZTfkmMFCwKu5JSAfswJkeg0VQzClBg7jlQeu1bfHzbsFJ9dfWvB12GFMb7qpMypUByoLxYOBNU2EkJLAqeaCE9szsRqRITB03Vo370XzvKl4qeUyfdCs8XVKOFMZKWGh38jtip/z6+pnz5FdGzME0xmf+1nOgikf1VsXnl2r/7eXGrV8w5EqwcBIEyGkJHCquRiJ6QW7yJsxtWMeweEYhTOrH3X4/UqNdPIoDtx3pb5d877FiP7T9TkdM6KEMUYJOXbH1VYrqB5V5Sld9/LfS/P7WGrjgsoViiZCSEngJIrKMb2QSw2W13SkWQfJinxbN+/NmguXTKtYsaGraDVKMgb2PoOe9q/p27FP/wBVp0zI6ZjVSgijXQimiBLG8gXT0dQQw1nLnnQdZTN+j0stpUwz2tyhaCKElAROosirh1GxkA1g9XrDdIq8uXHKthKiMnGab5dvJ4wz1/qPHkPX/R/D4OHXAQBjz/0XnLbgCzkdPywELjy7Fn/oPuwomMxz35yK440YRXwhOta8CHM2UuQORRMhpCRwEkXlkF4wCxmn6I/dTcwu8tbeEcdtj+12NEGsiWS7gNs5f3th7KgwBo6noEI+Q80LmmC69cw3cM011+j7J37smxg14WypSaRbUqqKZ/YdcjyGVue0tK0TKzZ0QQj3gtIs4vOdUvYSySq1qFe5woG9hJCSodx/CcuGphoRAF5qucxxKr3dsbwIiLAQuGbWZDROGedp6K0dIQGMrgpmaK2GqqrovmdBxr73fe3XOHj4aM6CKZ/EohF9Lp+qDpmIuh2CnCtehvRyoK97OLCXkDKn3MWEW8q95sJNBCEkhP552qVu7MaaeBEQKVXFI9u78eizB5BKByM9RleFAhVMR1/5M15fe7u+/fDDD+OTn/wkAHdCtJhsWzZXGsVZdH4M63fFPaeU3f579xLJGomNFPmAoomQEmckhtWDEInFEJpuUl8pVbWtRdJuYubuuFwJQjBpUau127tzPpbGKw9+DKkjPfr2hV/5JcY3zsioDStVoifSnzIB/Mj2btRWKxhdFcqIQNl9D738e/fSHFGOjRSlCEUTISXOSBt/4FUkWokjAEURml6G3srqgGoiCma3bMk4n6VtnUVPT113YZ0+HmTrnp6cxUyy9yAOPnyTvn3KBQtRO+dGvNqfwhIXo1yseNeEsfjrG/05rcstSkhgxeXTAdhHa3oHkogoYaxZXB9IA4ARL80R5dJIUepQNBFS4oy0sLqXm4ZMYI1RstNHuQhNt1GrpoYYdu4/hEd3HEBKVW0LpK3GnSghgf7jg3rhceb5FNcO4NEdBzLqonKpM3pj/VeR+NsOffuMW9ciXF2T0/pmnzMO21/slT4ei0bQf2zQsqjbbSG7scPPjcu6hpfvnpd/716aI8qhkaIcoGgipMQZaWF1LzcNmcCSRXrifQnMbtmSN7+k9o441u+K6zdguxtxNKJgxeXTM25iA8cHs9rhg6odCgkglwydOa2oIlNEzJk2Hm3PHUAyJX+TVOIIXvnmR/XtyDnvxYQrl/tflIE/dB+2vd5a7VHz47szfKqUkMjyrTJSfUKw2okMNxFGtz9yvP5791IHWO41g6UAx6gQUuKMtPEHspuD1X4/0TbzeBYnvIxvsXqujL5EEis3dqH/2KC+z8k/KBdGV4UQ0+bL+TyG+dw0wbRt2Vzc3TQDrVfO1N/DTO///L8MwTTpkw8FJpis1mZmdssW7Nx/KPvkxXBtkhUqBNYsrs8aR2PEOC5FhtsfOSPt33u5QdFESIlTSvOrCoGXm4bsRhSNKFnHMOJlZl0QHUoyegeS6Esk9Vl7+ZiFppFIprFt2Vy83HIZ1iyut73Be8HpnNXBJPavno8j29cBAMInnYopd2yCMq6w3994XwJrt3dnRcKSKRVCQPp98TPf0Pw5ehE9Qf17b++IY3bLFpy17EnMbtlSkiNyyhGm5wgpA0ZSWN1L7YWsuFUr0LXrvspHuiRX48hCFXtr36cg2vm162DlO/XW7qdx6Fff1Lff8dEWjJn8jzm9nx1OtUmyR/oGklizuF5agO70XbEyNZXVP7kh13/vI7HjtlBQNBFC8oqf1n+3Nw0ngWUnDLykS9x2Hc2ZNh6PWLTj51pPFAS11cMpqKDa+SNKGHOmjc+6xlZGlXW3b4QQ/mNpmgiRFaArYWFbT2XHpGgETQ0xrNzYZZkijVbL03eAdVrWmLosNCOt47aQUDQRQvJGIX7xOgmsXFutzcJMc35e2taJ1s17M0Ta1j09lseoiSioHlVVEM8hJQRYNdpddt5EALAshvaDVvxtNm9MvNSBNx77kr592oIvYOy5/+J4vHBIIARI1+U4rkUdEoZOdWFm0WX8LkgP7XCpSq3DtdTWU0mwpokQkje8FFHniyBqRJoaYti2bC7WLK7H0WQ6ow7JWFQuHYY7kMS2ZXNx/+J621qrIJA5E2iCbsWGrpwFk8BQN9rWPT0Zn2/3mqsyBFPdF9qzBJP2OUSUEEInAk8hAYwKD3WxhW2iUXapt2RaharKa5OAoceuvbBO+l04LJkxJ9uv4aV5oRCU2noqCUaaCCF5o5i/ePPhCO6U9pDVNKkAzrnzKaRUVXeIdjsENii0ax7E+06KRnBX+/P6uR7veRmv/uAW/fGaD1yL6OxrZC+HCmT4TqXV4W07YeRUs3Q4MVSbZI4KunXj9mvvUWrGkaW2nkqCookQkjfy6TFlJ4q8pgXvan8+w5DymlmTdfdrI04iUFbTBAyLAc0huloJYSAPhpVCWKeTgooyRJQwzjw1op/naz9dhmMH/qQ/PvlzP0NozEnS1/tNUSohgcUXTM5KBxrRapOs/LNaN++1TKkacSs2rGrChHDn6VQIaGSZPyiaCCF5Q3YT0oqHvf5BN96sjLUpZlHkpRD2rvbnM4SONuAWQJZwchKBspomM0EOuzWjqkN1F0Y5FgICiTJonWC3PbYbqbd78cq3r9cfq/6Hf8b4y5tzcgq3RQCNU8ahcco4y4JtWSTFi4B2IzasugSBoes+kExnjJspJiOp47aQsKaJEJI3rOqJtMnv8b6EZV2QDO1mpYkW843ZWCvlJS346I4Dls+12u/kIVUKhbZjR4Vhjl+lgSFjR9gbOdoRUUK6wWPP09/JEEyxT/0A4y9vBpA/24RkStVFb8eXL8H9J7ymnOrUvNbVafVrL7VcZmlo6WRgKvs+kcqAkSZCSF4x/+Kd3bLFUzu0l/Z4TbR4SQvazYYzr0Grk5FNrc/VpylXIkoYA8etb+iP7jiAu5tmYMXl0/H5ts4sYeVEIplGf38/TjppOPWmnFaHSR//Tg4r9oZRlMoiKea0ba4+XV5f52aGHSlfGGkihBQUN1Egzc34zGVPYmlbp2shookiL67idt1as1u24K725/UIl4pMF+/XDh/VIzja+yrhfPp6y0eghIXAqoUzpJEe48087GONR3ZuyBBMp99wnyfBFArgsjj5JRmjkU4u635rvJxeZ/d9IuUPRRMhpKA4tUM7peFkGGullrZ1YowSQjSiZKRvAGSNlrhm1mTpMeN9CTyyvVuajtHqn+5qf354Zx4DDUpI4NoL6ywF4b1Xz0RTQ0x609b2t27e68kEUk2nsH/1fPT+5mF935Q7NmH0xHe7PkZttRLIZXEK4shMJq2YM228rzVYCXIjdt8nUv4wPUcIySvG9JrWMm5nMOhl6K1xVIXZaFHrUluzuB5NDTFpQbAmprTuOT9oqa/WzXtz9kAChlvrayUt841TxmVc00QyhZUbu7BiQ5f0HLSbuZf04cBfd6Dnia/q2+Ov+CKq3/0+16+PKGEsOj+GtmcPOAoeNxjtEoxpuJqIAiG8DTx2W7Rvxlgsbu6ei1SFsHZ7N7bu6WG3WoUi1DznXxsbG9WdO3fm9T0IIcXByQtJ1mkEyGdznbXsSVdRCfPrZONStFEWTo97eW8rXm65LKfXaxi7r8y1VEYBZeXGbUVIAKOrQjh6ohX+4InUlRP7V8/P2K5r/gVEyL0xp/b5rNjQFZgnVVgI7Fv1r7bfK7cIAC+1XJa134+/l9V6Ikq4ogdrVzJCiF2qqjZaPcZIEyHEF25aue2iRtqNu//YIFZu7MLStk5MikYw5oTXjQxZS7usVirel7AdTGt8nd9Cbi31lUshuNkfqr0jjuZ1u/VUmjGKEu9LYO32bkfxU1ut4O2jg/r1dLO2Y6/9Da/9aMnwMeZ+Aqe8t8nTuRgJ0sRTi6L5iUaasUoT+x37w1lvIwfWNBFCfOGmldtNh1JfIonegeGxJG4Fk9mqwK5A121BsFO9iowLz67N6fUAcEqkCpt2v6rXW33x58/b1h65iRb1DiQ9pQsP/vDWDME0eek634LJKDjc4lRCHTvxWbntfItFI9IaMKumAL9jf5wEu5OdBikfKJoIIb5w0wUX9KwrO28mJ8GiIvumbL55ar5SXr2Mtr/Yi/aOeJYvVa1Dt5cRY1devC+Bfol1QD4YPPIG9q+ej+QbLwEATqr/EKbcsQmhUWNyOm4imfLUNff+c8bpwsjus3LzvYpGFGxbNhd3N81wPXvQ79gfJ8HuxoeMlAdMzxFCfGHnhSRz7s4H2q/55nlTsWrhDFtPJ62GylyvYq5jmT9zItqePeA6SpNS1Yw0jrHwvNR5c9O96O/aqm/HPvtjVJ00LrDjp1VACQtXHXvb9h3SU51acXffQLYflpXTvBljE6Fbd+wgZ88ZYaqucmCkiRDiC5kX0pxp431ZBhixijLYRX+MqaBty+bq0QozWtG30e3Zyttn7fZuz11w5jSOl7obvygh/75A6WP92L96vi6YRp9xLqbcsSlQwQQMXfPWK2fqn4nTerW6pb5EEkeTaaxZXJ/lzG2M6Mno89BJp+HF38uIm/WUgls8yR2KJkKIL6xGpKxaOANb9/TkJBYiShjXXliXddwVl0+3Tb85peq81LH4jYwZb4yFcAZPpuUO1HbS5PAzbThw/2J9e+KN38Lp194T8OqGIkxahEj7TLzYOrgZdyITKn5Sw7LvtJsIUT7WQ0oPpucIIb6xSnssbev0fTyzjYCZnfsP2fopaaLFy5T3ICMAk6IR3NX+fKDzx/ymN61eo6YG0f31puFjK6NR9/n1ju9fE1F8dcGlDCk5v5E3p89HNhTa74DiXAfdBr0eUlow0kQICRTZL2qnJNJ1F9ahed5UtG7em+HYrdHeEcf6XXHbSIWfX/M1PgfYmhEYdhAPcv6YiqGCcr9deRr9f/6fDME04eqvOAomjc7ll9imnmSkAazY0AXAvzh1+kxziQ7lg1JbDwkWmlsSQgJFZvS36PwYtu7pkaatohEFxwbTUoNAO68l83PdmA16GQRcbASANYvrMyJnA8cHXTlgq6qK7nsWZOyru30DqkdVIZFMO0ayaqsVVI+qkl6nkBgq9rbj5ZbLbD8/2RqCMoh0Y1jpx9SSVCY0tySEFAyn1JjMNdsq/WPsOrKLVJjTek5mg+0dcTQ/vjuQkSf57g7EieOv3NiF5Qum6+d4V/vzeGR7t+3rjr7ShdfX3qFvj5t3C06uvxQAdD8su7UrYYG3j9qLMzeXsL0jjoHjg9LHNTsIFcMjZJxStebjy75vbgwr/ZpaloLQKoU1jCQomgghgWNXF+LVNVsTS7LXGcegmF8jO9aKDV2BCKZYDg7gXukdSKJ53W4AQ9fXaXbaKw9+DKkjw8+pu+0JiKpRrt8vLASqQsLWbNQN1UrI1cgTzQ7C/Fk6YRbA8b4Emh8fvk5u3Lr9OHr7FVpBUgprGGmwpokQUlC8umZrNS1eOuJkdTDa/iBHe3gxsMyVZErVu8lkwjDZexD7V8/XBdMpsxZhyh2bPAkmYKgrL1fBBAADybTrAnA/dU9WAjiZVh1rqYz7nRy9rWrs/LqHB0kprGGkwUgTIaSgyKbEW2EURV464nLtYAqHBEKAYzQq3peAEhKuzRuDIG4TeXtj/VeR+NsOffuMW9ciXF1TkHUFgUzs2qWgZAJY2+/GsFL2HK24H8iO4vh1Dw+SUljDSIORJkJIILR3xKW/ys04edoA1l1H2uuM5pSy49t1MNlFh2LRCO69aiZar5qpvz4aUaSvSaZVjB1VlTE6xesYFjN2BpDaY8bIWypxBPtXz9cFU+Sc92LKHZvKSjDJRK2V+aiXsSRuIpRWz7GqVTNGcZyimYWgFNYw0mD3HCEkZ6wKq5WQQOtVMx0Lae067fJV3NreEcfnH+vMKGIOCeC+q+st38ep004AeKnlMsvz8cP9i+uxxMbvqrZagaoORVMO/8+P0Lf9cf2xSZ98CMq48qhnsRppY0bWdafVPzV85WnLQvXaagUdX74EgL/uOT+fdVDdfm4phTVUIuyeI6QEqaSuF7u6Ertzskq5zZk2Hut3xQte3JpWgSVtnVixoStj5pl5PVZov+yDGJ1SW6041qT0DiShDibRfe8V+r7wSafijM/+KKf3LkQnoIbbom+nFNTyBdPRvG53RnpUCQssXzBd33ZjWGl+jkysaZ+1l3RxviiFNYw0KJoIKQKl0vUSlHBzqiuxw+pm5bWTySsrNnRJW+WNa9bm0NkJCWOqJ9daEiUscHgg6ei/9Nbup3HoV9/Ut9/x0RaMmfyPOb03AESrFRxLpjAQQAG4E3OmjXf1PKeapHwJBzd1cW7EWL5/HOXqYE68QdFESBHw0+Lsh1z9a4pBIYpbvXTP2Qkms5eQ23Ej1UoIiWQaNRElI6p1qP+YbUG5tVHlRgifQ3vNuDHLDAqjZYI2eialqggLgWtmTcbdTTMABCdevBKEGMvnv7FKilSXEywEJ6QIOLU4uy1ytUOrMzIW0DY/vls/dpDtyrIiaT/t+OVS3BqNKFnF6G61y0AyPWToKACtrHTg+KBti3/ipY4MwXTagi8MWQkEJJiCJiwErruwTjo+R/s3oJl0aqNnUqqKR7Z34672YXFRrLEkxsYDuxE/MvJlCZBrcTzxD0UTIUXATgAE9QcwCP8atyxfMB1KOPP2aK4rcYsXPya/BOGtZKVVvEZqegeS6EskoTq8tnvNVXjjsS/p23VfaMfYc//F03sFTW21gmpFfgtJqSrW74ojKrnW2r8B2XBj4363XZP5wq9IyVfUlP5MxcO3aBJCfF8I8YwQ4q4gF0TISMDJ4DGIP4Bu/Gus8BPRaWqIofXKmRnRgNYr7Tvn7I5ljCzUVisYXRXC0rbOwKJwl503Medj9FmIHDurAD8c73kZ+1fPh3p86CZb88HrhqJLYX+VFWEhMPuccTkP/wWA6lFV+NrC83D/4nqpdUQimYKqwlYEy4Yb+x167MX6wu3r/IoU2TBo434/66U/U/Hw9S9PCLEQQFhV1fcLIb4jhHiXqqp/DXhthFQsbgwe8/0HMFcDSDN+60pktRlWg3eDqglxGkHiBitx6fdGb8VrP12GYwf+pG9PXtKG0OixOR1z36p/BZBpoeC3Y077LFYtnIFty+ZKZwoeTiSzhg0b62+MKUojfvSn00gVu9fZfc/8ihTZOWj7/X6/3Rh2kvzgN9L0LwAeO/HfWwB8IJDVEDKCcDJ4zPUPoFOdUTFrRTSc0h5BpSHMv+ZznRcnE5d2Zp1uGXz7EPavnq8Lpupz/xlT7tiUs2CyioIJDEU9/KYr3Zo92qXXIlXWtyHZfjucUtIynL5nfqOyVtFI436/3+9CpLCJNX6758YC0GKIRwC80/igEOImADcBQF1dne/FETISCDrioxGUf00+ceoitPuF77Z7yOrXfC5+ROaOOSOyz/I9dTXY/mKvYyTq708/iLc7nhx+r0/9AOMnxgLpakupqi4Yjeffl0giooRRrYSkVgO11Yp0Ddpn5Pd7LCt+9zL3Tvsu+LW+cIok+T03p4iQ3wiWOVIdFiJDbLGLLn/4jTS9DUCT2CeZj6Oq6sOqqjaqqto4frw7Lw5CRir5ivgEWWeUL5xuGrJf8iqApW2drgpzrYSZCmR1dUWUsKfxJ1a1KLLP8qrGOpxeM0Y6kiV9/Cj2r56vCybltCmYcscmVNVM0F2tZWgRJK1bza6uSruBW40HkQkmAaDjy5c4RkT9fo9zra0zRiv94rQGv+fmFBHK5dybGmL68TUxzi66/OM30rQLQym57QBmAmDJPiE5kK+IT74jSbl6xTj9Erf6ha8hmwtmfn+ZMFORPcYDgOMYlHhfImvEibZvSVsnIkoIxwaHLAVeO3wUj+/sxh+6D+vH1CI7Gkd2bkDvbx7Wt0+/YQ1GT3wXAOjdaWEhLKNUYSH0OiWNR7Z3S9fuh5AQQ7VC86ZaRi5z9UvKNdLqxoXdKf2YLy8oJ6+nfJx7PvzeyDB+RVM7gN8JISYB+BCACwNbESGkqOSS9vJapO1003BTMG8kfiJtZ3x/mTCzG+Nx22O7fRd1G9NKKVXFtn2HLJ6TQkhN46V7Ls/YP+WOTRnbo6rC+nGsMO7XPregSakq7nzieSw6P5atVF1eIrvvVK4mkk6pLDfWF/kcR2IntvJ17uyiyx++B/YKIWoBXAzgt6qqviZ7Hgf2ElI+eBkA6jRI1ct7urlpyLqzzJjXq5knmrnuwjrdddqM7DVBMfDXHeh54qv69vgrvojqd78v63nacFinax3UoGA7ZNEup8/by3fKT+TSrrDfrv6sEgjq3yDJJC8De1VV7cVwBx0hpALwEu4P6leu27SH3eR5I+b1yuwFjPvNN+v+Y4MuV++d/avnZ2y/7z+fxsEjx62fLKCnxszCQ2AosnbOnU8FanUgQ/YeTp+32++UXeRSO87BvkTW6BmrgcoyUVZp5KuJhMjh7DlCiI4XISQTMSqGfgH7+YVvF2mwqqlxcx5OI2vM3WS52hHIOPba3/Daj5bo27VzP4lDJ2qZGr7ytGV3mqoOFbyrGDb57EskM9brJJiUsHB1zfziVLTs9jslE1crNnTh2GA6oyZMI96XwPpdcSw6P4ate3pKeg5bPmbF5TOtSKyhaCKE6HgxzbMr0vZT3+SqRsrlvd+4Xtk5aZEaD4f1zcEf3IJkz8v69uSl6xAaNQYNX3kaqmrfEq+trXdgqIDcrv3finwKJu0a2olk2fXXCsy118jElZNdQCKZwqM7DuDeq/PbGZqL6Mnn4N5i24aMNDh7jhCiM2eatUWI1X5jG7YVdiZ9Vu36TkZ/rZv3ZhkXWmHu6GqeNzVrLh7gXSgpIe8W1YNH3sD+1fN1wXRS/YeGjCpHjQEwPHvOLYlkKhDPJg0fp6Rjjs7JWt1lI4O0AnPtNbmYuZqP5Qe7cSa5DsjlrLjKgaKJEKLjpv7HiOb07DTJ3ojsBuQ0TsZ1rZSVGgog2JIGPPk4vbnpXsQfvFHfjn32xzh13mdzX0iAjD9plPSxWDRi26ovs3wwo4lrK/8o42tknkZuhV0uIiTfzvTscqscKJoIITp+/7h7MemT3YCcju02EpFMq2jdvFePHCxp63QVoXIilVaRTKUdh92qx/qxf/V89HdtBQCMPuPcIaPKk8blvAZgSLjJ1uB1EO/rb1kXoGvdV8sXTPd0TM3ywUxTQwxpSe2Vlt4DYGkg6eWj8ytCnERRrqInyOHYpLhQNBFCdPz+cfcyC8vLjc14DFmaxwqn6JVf+o+nMEaR/9k8/Ewbuu9frG9PvPFbOP3aewJ7/4gSxorLp2ekRbUIjiYygkD7jJxSsFZYpa3aO+IIOTiVazU+5hl1Xt7brwjx60zv9v28pL1JacNCcEKIjt8WZi9dPG6tAzSPHWCoG+9gXwJRQweZHdosrnxg2eWWGkT315v0baGMRt3n1wf6vmbPIVnx74oNXZ7qpKwwigGt0NitT5bZTqC9I47mx53NQmXWFlbfSSUkAJFZ5J5Lq70fZ3ov7+c17U1KF0aaCBlh2BW85msOnhE3ESMB6OZ8xlqT3oEkjg2mEbGJ9hhncRWC/j//T4ZgmnD1VwIXTLXVCprnTUXr5r2Wn5uRFZdPd120/q4JY11HCL1EcYyRmxUbulynR60iPlbfydarZmbNVczle+oUKc313wVrmioHRpoIGQFo3WlWnkTm1mc/LcxeWqrdjEbRbtBe65/cPB4Uqqqi+54FGfvqbt8AIYL/LXo0mbK8vjv3H5L6E7kZPTNwPI1F58fw6I4DSKkqwkJg0fnWn7/MYNNKDhkFlpeolxdh5uZ76tYmwE2kNJfWfi9WHqS0oWgipMIxCxq3g2694HVwqHYDko3Y0H7hl+ov8aOvdOH1tXfo2+Pm3YKT6y/N2/sZ59kN70tljHqJ9yXQ/PhuT8fVzCG1yFxKVfHI9m48sr0bYSFwzazJ+qiZpoYYdu4/lCGwLjy7NmMYMeA/TSZ7nV+PI6+vy6ffEZ27KweKJkIqmPaOuKvhs7mKE7/pB6sbsTHSITdGhKeuqiB55TsfQ+qt4VqUutuegKjKbN3Ptwu3jGRaxX888UeocFfTZVf7pQkoALi7aQbaO+JZAusP3Ycd3bhlZpxjR4URrR7lGAXyKshzfV0+oHN35UDRREiFov3SdlPf43f0iZb+kL2DU/rB6ka8flccjVPGoakhJv2Fvuj8GNqeO1BQYZI8FMfB796sb58yaxFq/+VjWc8LC4HF752c14G/dgxYRKWsiChhV8Lq0R0HcHfTDKkI2bqnx3Y47PIF07PG3yhhgf+8YrgmSPseLW3rzBIUfgV5qdUR0bm7MqBoIqSMsavZsLrJ2eF1tINVas2Im/SDUzTA7hd645RxWNLW6eLMMgkLgZSqSutxrHhj/VeQ+Nuz+vbkW9ciVF1j+VxN+JUyQgx5Irmpe9IEbS7RREAeZXFKo/mtB2IdEckHFE2ElClONxs/v6it2sX9iDJze7wM2Q3buF/2C11L7XmN6KRUFREljPfU1WD7i722kbhU4ghe+eZH9e3IOe/FhCuXO75HoYrRrRBiaNCvLeqwmLETvsCwD5RXEeK2CNtJOPutB2IdEckHFE2ElClONxu3fkhmNLHlV5QZ7QKc0KI+VvvdoBUpazVRxuiRAFA9Koz+49mCIJFMYdu+Q7bHfrtrK/6+6V59e9InH4IyrrTTK0pY4IIzax3PrSaiuPa+umbWZADeRIiXImynCJbfeiDWEZF8QNFESJHIZWo64HyzsbrJucGp3V8bLRGSCB4v9VGyKI+bOiy318+tKaPGxJMVdLQsxrG3+wAA4ZNPwxmf+X8ejlAcwkKg9cqZruah9SWGBwX3DiQRUcK4f3F9VlG+U/eczJ7ASxG2mwiW33og1hGRoKFoIqQI+G2jNuJ0s9GOs3Jjl2X3khVu2v21tdoJG7fnE5Ocg9PoDC/XLyrp3rIisX83tv/si/r2pI8/COW0ya5ea0QbNGsV5QKGHK0vOMs5IuTl/TSzxaU+6rw0QbNt2VxdJJlxKto34qX+iWk0Uk7QEZyQPGHnvJ3r1HTA3by3poYYOr58Ce5fXO8oRMwux7JaFbcjStycj9U5CAwPcZW5Xnu5fm7MwVVVxWs/+QLeOCGYou9qRN3tG30JJu06XvEea7FYrYTQetVMrP3k+3DdhXV6KjIsBMaOsnZKt0tXuv3cnHBK5bq55tp33ks3ZSFc6AkJCkaaCMkDfuuBvBRvu6nZMKewrruwDut3xbN+1VvdpGQRAC/pPuP53NX+vDT1I3MrX9LWiRUburDi8ukZ6/Ny/ZwcqY+/8RJe/eGt+vbvfvc7XL/psKeUnhFt3IlMhIyqCuvncnfTjIzIjszsc9H5sZw+Nzc4VZE5XfP2jniWtYARu+gR02ikXKBoIiQP+C3S9holsLvZWAm39bvijmaExmNr52J8rps2dfP53NX+fEaXm9k4sakhhtktWyyP25dIZqXevFw/WbE5ALy56V70d20del7NOxC76WH825NHpAXkbmh+fLftrLW+RBLtHXHP4zwap4zzPRKk/9igo3h0EolO13zlxi6pYHLbTUlIqUPRREge8FOkbfdL3E/RuF8zQiMyUeZ16vyjOw5YHv/RHQd0MWAnxMxFxF6un5VgGjzyBuIP3qhvn9Z0J8ZOna0/v/94yrfruJvhtG7GebR3xLFiQxeWtHViSVsnaqsVLF8w3TaSqH0vzJ+bUxTIDU7X3K5uzO33jZBSh6KJkDzgtkjbjRDyWzQeRApQdlM2d1EtvmCybSTErkvObSrJuG4v189cbN73u7U4/Myj+vbkz69DSBmT9To77aOEhqJXfke5OI3zaO+IZ0WsegeSuM0wW27Fhq6M6JHj98JhrbXViu3jbOEnBBCqmyrJHGhsbFR37tyZ1/cgpNSQ1ab4KXCVpa1i0YjtL3i/r9PIpb7GLLYOHk64Ksh2wk+aRzuPt986jFe+8RF9f+1FN+GUxst9rwGAp1SlGQHgpZbLLB+TfXZu12f+fJ2Op4SH7ApyEUD1K5+2TAFGIwo6l1/i+7iEFBohxC5VVRutHmP3HCF5IMiOIDcRI6tOPTfddXbI0nuP7jhg20WliZR4XwIqhiIgQf02i/clsLStE2dadCTKaGqIYa54PkMwnXHrWl+CSTPuDCK6Yle/lst8NKvX2h0vFo24Fkx2HaErLp8+lKY1oIQEVlw+3cPqCSltmJ4jJE8E1RHklOqTpe9WLZyhzxdz213nZlCqLNWm2QT4jZAYCQlgYo31eRu765rXDaWrtDWbO/SuapiA+2/4AI4dOwYA+PD1N6Pn3Kt1J2xVde6uM793/cqnkUylfReKA5m2ClaRM79u7tprjbR3xKVGpOaolN33wSlNzPQdGQkwPUcqnlydt4uNU6rPbxrO73Fl3WheBuC64eWWy1y5eddWK+j48iVZHXqJfc/hjXUr9e19+/bh7LPP1re170UQIi8IjKnH9o64r2HEVmlSWc2Ym+cG8T0jpNywS88x0kQqmiCct4uFUezVRBSMUULoG0i6jgiZ03dW1gFeB6UKIGvGm7Y/SMGkeTm6ibhoXVuaYFLVNF79wa1IvrkfABB59/swsPeZjNfYiQmNiBLCuLGjCyaqzN/NO5/4IxLJtKdjmFPAsqHKYSFcPdf4fQiisaBYlPsPJ1I6sKaJVDRBOG8XA3NdUF8iiaPJNNYsrs+qqZHVxhjTd82P786oMdK2rTAOStXqsoBMYaRi2AwxFo0EKpiAYRfv5nlTEQ65G94LAMde/Qu677lcF0yn33AfJlzxxaznycSEEa+CRcP9aq3ec/i7uWrheVk1Qk64HWeSVlXPo0+cvmelilWN3Z1PPO+qHo4QMxRNpKIp11/HXsSeU8H3ig1dWd5BybQqvbmbB6VuWzbXUhipGE7NRCP27ep+2bn/EFIOff3ae7/xxN147cefBwAo489E3e0bMHriuy1f4/bz9xJliihhRCNKzgJSq3UCgNarZjqOv9EwPs/POBMnUZRrY0GxKNcfTqQ0YXqOVDRBOW8XGi9iz1yAqxU4L23rROvmvdJCZxXZY1EiShhzpo3H7JYtGakMp/UcH/RfFG1FtRJyVVSuhARubhgLYZjNNuHK5Yic8159WyA7PeNliK/b9X5t4QxfdUhWGIv5tXohY5G7GSUsdPHilHqUCR0n88pyLfQu1x9OpDRhpIlUNOX669hrKkSLCK1ZXI+jyTT6Ekk9FWGH2RZB82AypzJqJJEkbT0DPlNZALJSUKETzuJOaw8LgVP/3IbPfPgDAICqUaNRd9vPMwQTALz/nHFZ6Zm3jw5CCeeSSMukduxoNDXEbAfresUcDbm7aQb2rfpX3L+4PjttZ9BRdqlHO+sLNzYZ2vfspZbLArNeyDflmlYkpQkjTaSiCeLXcTGKSL2OWdFwU6ujUVutZNkizG7ZYpnKGKOELKNSuYrPWDSCOdPGZ9gEjK4SjiIsNXAY+x+4Fi+e2P7ud7+Ll0+bldE9p9HR3ZdVn5RMq4hGFIwdXRVIobcWtZDZMeR6XCOtm/dapludCrY1jykzlV4k7fffEiFWUDSRiicXv6Ridd/5FXtuUw5KWGD5gmzTQdnr+waSWLO4XrqeWh/pLi0VuH5XXBcbKVXFQNJeeBzZuQG9v3lY377gy+34xCc+jHPufMry+TIBdjiRROfyS1x10jmhRS3MI1vcEFHCGKOELK+fVTTETcG225R0OXeXuqVc04qkNKFoIsQGpzbsXDD+wtfqkA4nMi0FvL6H7IapRVWcbhp2N1y79SxfMN3VQNhYNOJoeyAjnTyKA/ddqW/XvP8jiH7wOrx+bChC5jXKE61W9NqtaLWC0VUh9CWStvYJY0eFkVYhjVpYRTWy3tfiswBgef0Gjg+ivSOecd1l9VjRE7PjZFYRVmaa+fx+B0mu0bCgjGYJoWgixAa7X/W5/CE3/8I33gRz+bUvS0WsuHy6q2P5TWUYb8KySEs0omSlh5a6LJzu37sNb7av0rdjn/4hqk4Zr297je6EQwKHB5L6de8dSEIJCdy/uB4A8B9P/NEyQnXFe2L6YOJ4XwJhIZBIprBiQxdWbuxC30BS99TqHZALMCtndqsn9g4k9e8CMHR9ZRE9TTOaPwvjGszfrXIokh4J0TBSPrAQnBAbZMWiNRElJ+8XpwiL35boXGfemV9feyICs7St03HWm1YkXFttXTRuVSPtVIyrplN45f9+XBdMY6fPwZQ7NmUIJj+k0irMkiiZVrFiQxcA4Nigdaxp654e3fgzooT16FZfYkiAqYb/BoaiP9VK5p/ZvkQy67tiVaekkUimsHJjl/59k3HY0CVpZxVh/G4VqkjabmadE7QMIKUEI02E2CBLdVi18XtJa7j5JR/vSwwNpj1nHK5qrHMd1co1FaG93u8v/D5JJMRqf/O8qVlpqXBIIATgre4/4fW1d+j73/2p7+BYTZ2fU3JNXyKJ5nW7pak+7XNzm1bUok1mzN8Vp++Dm3oxP/VPhSiSzjVSVA7RMDJyoGgixAa7VIcVbv+QexnIum3fITyz75A0xRIU5nRj/7FBX/Uunr2xTBdUqCre/sVKvP7CcwCA0bFpOPP/3IujkuhP0NjVZdVEFLR3xD2lA2VHM35XchnQC8iFjtNnUYgi6VzrpsrVa41UJkzPEeKAXarDjNs/5Fb+UXbYpViCoL0jjuZ1maNWZKaY5pl25rSLF28sc1oq+fcD2NcyH6+dEEwTFt+N06/7esEEkxPHB1MZNUa5oAK210xDcxqXYZeCdfNZ5Nt7KddIUaG81nJJIZKRA0UTIS5x+iPv5Q+5Ve2Q11EkQaYnVm7scux80zDOtLOq6wKyTTNlN3XjOfx987dx8HufBgCEqmtQ94V2RM6sz+m8gmYgmfZlTSCzvLS6ZkbCQmDR+THMnznR8vXXXVhnK3RyrXELglzrpgpxDpxPR9zC9BwhLrFLocQCaoM+c9mTntZjhZ+uPi8eS2eeOvS+dmkXqxu51bomRSPY/0oc8W/foD/v1Pm34aTpcxzXEY0o0miYHZrtQaFiVxEljEXnx7B1T4/l98d4zQBk1P+kVBXrd8UxRrH+fbt1T4/j+xe73T6Iuql8n0O5WC+Q4sNIEyEukaUJ7l9cH1haY/Y541w/d8607A4yq1/MS9s6hwrKA0o5bH+xF4C3tIvsl/zoP2/MEEyTlzzmSjDFohF0Lr/E9TBbjdrqIdsDO8GU6x/FsaPCemRJixTd3TQ0Q04WcbIrME8kU1JRWw7F0KUQ7XKCxebELYw0EeKSQhTNrv3k+3Dtd/8X2/YdcnyuVZTB6qZrLiDfuf8Qtu7pyTgHpwJ3I1pnmZcCXfO60scGsGf11dhzYrvu0k9AzGxy9f7G4bRzpo23HJ0iQ9bZZyQcFki7TFWaEQDS6vC11CJFjVPGoakh5njNvN6ky6UYutjRLidYbE7cwkgTIR4oxMDStZ98nzQiYcTqBut0000kU1i7vTsr4uNFImhDab0U6BpvSG93bcWB+6/WtxvvfAzf+NoKV+cMDHW3LTnhG7Vp96seVj58E4xI0l0C9t1zTqiAradQ87ypWcN2ldCwCJTdpKMRpSwHT5cL5TrYmxQeiiZCShA3v3CtnuPmdVadeGEr50kJ18yarNcnGV8bPeGEbWWEGRYCamoQB775Ufx9070AgJPqP4Qpd2xCT7oazet2o8ZjIbxdh58VRnGyauF5ls/JV51Thpg1X2rDtuzmveLy6SWf4ipnyiGFSEoDpucIyZF8TIl3mmGm/Qo2v7c2ANdrh1dKVaGEhNSVWmP2OePQOGVcVrGyEhY4cjQJ7eXxvgSa1+0GMHRDevulDrzRdpd+nEkffxDKaZP17WRKdZz7ljMmsaKEheeoUrUSggrh+fpqgrB1896s90ymVL3g2C4FfFf783jt8FGoAF47fBQ79x/iTT1ASj2FSEoDoXoccumVxsZGdefOnXl9D0KCxq0QMrsdA0OCJohfqcY11EQUCDFUk2Mc8mr13sZOLbciRAggJARSDqIpooT1uWpuiEaqMOaXy7Fjxw4AwJgzGzDh6q9A2ES2tDXHThhs+umQk69naFiuHyNJJSzQeuVMAEMWDeZroIQERlWF0H88W1DVVivo+PIlOGvZk5afhwDwUstl0ve+q/15y9qt2eeMw8t/T+Stxo6QkYgQYpeqqo1WjzHSRIgJL2Mf8tmq7PTLd3bLFsv33rqnR29fN4s/mVhQVUhHh5iP7zbKcvyNl7D7h7fq23X/9nWI06c5vk4F9Pl1QUef+hJJTyIsLARSqpplKaGNmTELa9kAYq0A3W/B8aM7DljuNzYMcJAtIfmHookQE16EUDFblWUCyLjfLLxmt2zJaVyHW97cdC/6u7YCAM4++2z85S9/wcY/vqaPo3Gid2B46K2K4ehTXtN3JmLRSJb4XNrWqQuknfsPZaXLnESRX88iN4IWoLcQIfmGheCEmPAihAo1Jd4KWfG2XVG31/EtVlh1cmkMHnkD+1fP1wXT+KY7sW/fPoTDYb3z0A9aum7N4nrP3kx+EBgSn7NbtuCu9uezPKY+/1gnHtnerYuZlKrike3dOPPUiG0XlpUT/Ogq6+J5I14K9ektREj+oGgixIQXIVTMVmVZ9MEuKmG8aftBCQm9k8t8I+/73SOIP3ijvj358+tQPXV2lhjQUm9eOdiXyEl4ucUYzYr3JbB2e3dW5FFW+rX9xV7HLiztHNYsrsfRZBp9iaTj6I5rZk3O2ifDq2DnzDVC3MP0HCEmvKRQCmF4KSMmSQU5CSItZScrStYIARAhU3G4GD4GADSv242j/W/hlW98RH9K7f93M045f4G+ba618dt7og24dRKkuaTwtBom8/u6JaWqrruwvKSB726aAWCotslOFHsV7F7q9wgh7J4jxJJ82AgETa6de3b1TbFoBAPHB6VdcrET9gYPP/w99Pzym/r+M25di3B1jeVrculcMxJRwjijdgz++kZ/TsexOq6fYbxmrOYQyorGnTrprF4nqwsLC4F7r57p6Xsq+w4Y67kIGWnYdc9RNBFSxuQi7pxEl10kSh08ju77FwOpIVF1ynuvQO3cj+d6Oq6JRSM489SIq3EzZsJC4JpZk9E4ZVzWtbvtsd2ui67t0K4jAKzY0JXVsRdRwhBQMZBMZ71WsyeQfT6Lzo9leXH5tbnwa4FASCVDywFCKpRcDPmcUouyTrCBfc+hZ91KfXvSzd+DEj3d1xqscJNei/clMHB80PY5ssiRVrT95B9fxfIF0zOu3xKJZQAgT4dakUimsHJjF44m05ZrSCRTkNV2a5pNlr7buqcHqxbOCCQSyplrhHiDkSZCKoB8pBPNkQ5VTePVH9yK5Jv7AQCRd78PE674YtbrohEFxwatxYIVtdUKVBU4nEjqruZtzx3IaQaclqpysjgwR2ic0lX/8KVfImERHQoaO4EWRBRI+75YGaAGZc5KSLnCSBMhOVDq9U35KuY1RqJefGE3Xvvx5/XHTr9hDUZPfJflDXfF5dP112nXrG/guK1Ttvl82p61NnN0i9tuM3PhtVMTwNECCCYAtm7uk6KRQNOyRv8rq3osQsgwFE2E2FAO3UVBu5Ibb8jRagUvProSR/Y8AwBQxp+JiR/7JoQIZYxssbp5G9+7fuXTALJFk1Wgu3XzXscZeHZcd2Fd1nw8O4y+Rn5TlmYiShijq0KO7uN2xedWZp4RJYw508bn9J20+r5ogonF34TYQ9FEiA35HJMSFDIzQ82c0W8UItn7KjpXf1J/bMKVy1F9zns9RyTuan9eKh4OW+zPpbsuFo3g7qYZliNmZJjrd+zqxJwGKWtrkM0GtHqeXQpRu9bm7rlcvpPFdLEnpNyhaCLEhnK4wdhFP7xEIdo74nr32KEt38Nbz7UDAETVaEz+3M8gqhTPEQnZoFnj2s2pJllaSgAY42AL0Nt/DO0dcdefj1dfI+0ayuwCohEl69rIuuc00dnUEPPU+i+bb+f2nFn8TYh/6AhOiA3FHJPiFqfRKIlkCkscxnRoEabj/X3Yv3q+LpjGXfrvqLttPUTVsIu3F8EoGzQLZKaajCNKpDYHgKOb+UAyLRU0GpqTuZVbtxvsnm+OnDU1xNC5/BLcf2L8i8wl3IuzfK7fyWK62BNS7jDSRIgNfgesFpKmhhh27j/k6BatRZ127j+UVYfUunkvXv/fn6P3Nw/rz5/8uZ8hNOakrOOYnbmt6n+06JHderS2ebdptFg04srNXLY/yK4wr9EaJ2sIL87yuX4ni+liT0i5Q8sBQhwot+45J8zpr9FI4i+rr9C3a97/EUQ/eJ3jcZSQAAQyrAG0Yzt5LYWFwL5V/+o4ykV/r7BA65XDbtd2buZWGOuMcv0s2zviWLmxK8stvZCt+qX+nSSknKHlACE5kIuBZCHwEq0BMsVM/95t2N++St+OffqHqDplvL4dEsApYxTLQm6rDjfV9P8yNEsAt91o5gO6KcjWEAC2LZsbSCekTKBGIwpWXD69YN+TUv9OElKpsKaJkDLDOJW+fuXTvrrN1HQKr/zfj+PNE4Jp7PQ5mHbXLzMEU0QJ476r69G5/BJIzKs9ExYC111Ypw+gtaqvsXqvZFpF6+a9+nZTQwyLzo9JXbWNaCkzu64zt8gE6tjRVRQxhIwAGGkiI55CpjrcvJfdc8yRDjsfICtnbgEg8UoXXl97h75v4scewFnvPlevbbJ632i1Ih3e64XTa8agcco4fduqvkYmAo0F6O0dcazfFbf0eTJirPXx2glp9TmUQzclISR/UDSREU0hzSvdvJfTc9ym4qycuSfWjMFrbXfh9d3bAQCjJ03DO65rRfWoqoz2dyuCKn2M9yWwpK0TKzZ06eks8/vK6pWMRday6xCNKBg7usqTMaVV8bbsc5CJx3x1U7J2iZDSwpdoEkK8A8A6VVU/GPB6CCkohTSvdPNeTs9xG9FYdP6wEGlqiOGFF17Aueeeqz9+7sdbMXDaP7i+EVuZUDphVwzel0hKxalVvZJAplmn7DocTiTRufwSy8e8dJ3JPofRVaEsF+98dVOWgxs9ISMNzzVNQohaAD8CMDb45RBSWAqZbnHzXk7PcRvRWL8rrnsyfepTn9IF04QJE3D8+HF0fe8LeKnlMmxbNtf2BqzVT3kNNMWiEaxZXG9bCyWrJ2pqiGX4MRnFlxapkq3H7voYjyvzS9KwE2Vuj5ErQdRgEUKCxU+kKQVgMYBfBLwWQgpOId2R3byX03Pcdo0lkin85+PP4Ir3XK3ve+SRR3Dttde6Xq9XKwMNrVsNgO2IEEAuTpycsq3QzDJnt2yRprPcdp3ZfQ6F6lxj/RQhpYdjpEkI8ZAQ4r+1/wFYoqrq4fwvjZD8U0h3ZDfv5fQcc7QkGlFgxeHt67Bz1bBgOnLkiCfBBHi3MtAwisA508bbPNNZnLoVCLFoBIvOj2H9rniGu/idTzwvdUG3oxRcs8vBjZ6QkYZjpElV1Zu9HlQIcROAmwCgrq7Ox7IIKQyFdEd2816y5wCQRlCM0Zj0sQEcuH9YLLW0tOCOO4Y75cwYC42j1QpUdSgF5do/yYRZWGza/arr51rhdh3bls21HNLrtz6tFFyz3dR2sbaJkMLi2xFcCPHfqqr+i9Pz6AhOiD80QRPvS2QVVRvdp7U0Wk/nr/H3Tffqz/n+5l248ZL32B7fLv3m5Optxf2L6zNu5Gcue9L1c/2sEXB2FxcAXmq5zGnpJYnb7wAhJDjsHMFpbklICaKJBS3KYhYDxoLgy/5xAuLf+qgumN4x63L8/A+v2AomwDn9po1DcUs0oni6gbdu3ouzlj1pO0jYXBRuRUpVMbtlC2okqcpyTmc1NcSwbdlcxKIR2+8AIaQw+PZpchNlIoR4p70jjtse22077BYYStNM/8TX8efvN+v7XnjhBUybNs3ymG6NGo2oGKoXOtiXQE1EgRCw9ClSQkL3hTJSa2OKqQlCmV+VVWrsrvbnLQcTx/sSUMICSkhkjHcpteHKfmFROCGlAQf2ElJCuO1YU1UVr/3kCzj+6lCkof7Cf8YfntkKYTFXxOqYESWMMUrI0eVbZhZpTBuFhUBKVRE9Iar6BpIZtVjN63ZnDPWVEYtGLGfEaes1pqJkXXW11QqqR1mbW2rXIhdH9mIhO1/tmhFCgoMDe0nOlOKNpBJx07F2/I0X8eoP/13ffse196D6H8+3FEyyY8qMGo0oIYH+44P6qBariJBspIv23FULZ6D1ypmexqS4MQGVRVj6BpLo+LK1uWUQjuzFwosxJyEkf7CmiThirK/JtZW7HDEOyLWrvwkCu04xAeDNTffqgqkqejrqmn+BMWeca5umcWvUWFutDEWLMBTBOGlMVVaEyFhH4yTwjEJn27K5WLO4XvpcYLj2yE0qyk87vhuzSNlzVmzoKth3wAovxpyEkPzBSBNxpJCjRkqNQkcetFSXmcEjbyD+4I369vim/0D11Pfr23Ziwa9R41mSzrd4XwINX3na1QBfTeg4pR2NURM3JqB+Ii+5OLL3JZK2EbdCUChTTUKIHEaaiCMjuQi10KMsrART329/kiGY6j6/LkMwmcWCMTJWv/Jp9A0ct3wvze9HFjWxE2JuBJPxGHZRKXPUxMpY0uhP1N4RR1NDDIvOjyF8IiUZFiJj3p6X8zE7sruBnWuEjEwomogjI9mZuNCC0dhanzr6Nvavno/D/9sGAKj9/27GlDs2QShjMp5vFBzmVGpfIon+4/IUml2q1Uq8eMEo5mTXSxu5Yjb4tJs9d+cTz+Ou9uexfldcF5kpVc2Yt2eFX0d2GW6+A4VM7RJC8g9FE3GkFEZKFItCC8bmeVOhhATe2r0Zr3zjI/r+M25di1POX5Dx3NpqJUtw+Bl94nZwrhtqqxXLmhuZh5Jsv5M/0aM7DniOALqpC7J6Tm21P/+nkV4LSEglwpom4kgpjJQoFoXuWjp+7Cj23dMENTWU/jrlgoWonXOj5XONKTKjBYAfghicW1utSDvXJI190v1O65J5WBmfL+v4dPremp8js0Bw+g6M5FpAQioViibiipFahNrUEMPO/Yd0Q0U3tTN+efLJJ7F4/nx9e9LN34MSPd3xdW69nexwippYiUczdpZvfZIaKNl+47qsxJqsYD5aregCzyqtB3gv3vb7oyHX1C5tPggpPSiaCLGhvSNuWTvTOGVcYDewdDqN8847D11dXQCAyLvfhwlXfNHxddETqS0/KTkjdlET80Df0VWhDD8mI4cl+4EhMWNVPB41pb7MQmHOtPFYvyueFeVZdH4sa78SFnj76KD+PrKxI34+Nz8/Gtx0AcooVb8oQkY6rGkixIZ8d88999xzCIfDumA677PfcSWYgKEibzdpMzNjR4Uz/Jhkfj/mmpzegSSODaZ1sWZGi/JYFT3LolDG/VY1QOt3xbHo/FhWHdLdTTOyao/GjqrKGKFiRbwvgXPufApnFqAwO5dawEJ3bRJC3MFIEyEGzJEOJ/fqXLjiiivQ3t4OAJhw5rsxdvG9OGwxIleLrGzd02OZdjJuG5GnsEa5Gr0hu3GPUbKdxM1RHnNkRBaFMu6Xvd/WPT2W6zVHf2S+Uma0a5Lv6E0utYAj2eaDkFKGoolUJH7qQaxSIjJBkkv33L59+/DOd75T315817exPTkFaYvnhoXQhUPzvKmWxd4qkLVOu/Eo8b6E7nVkh92okjWL6zNmz1nNljOmw9ykqnIVCnYiV4bZ4Tzo+iG/tYC5pPYIIfmD6TlScbR3xNG8bndGmqd53W7HVIxVpEMTJEZy6Z677bbbdME0aswYXPjVX2J7cor0+eaoiEwUqEBWCsvOKsBN67ud3UJTQ0xPP8k62YBhweMmVZWrvYPMFNMJ47V1Yw1QCO+lkWzzQUgpw0gTqThWbuzKinwkUypWbuyy/dUvi2hogkQWhXAT1erp6cGECRP07c9+uRWbB8/Fq2+7L+BOJFPSlJts2r2s481NUbST3YKbAnRN8JhTVTURBUIAS9s60bp5L5rnTc3Z3kGWDnOyYtCieUbM18do6RBUV56fc2EROCHFhaKJVByyER9Ooz9kKRGZIGnviGPlxq6M41rdRL/5zW/ic5/7nP6cC778C2xJK0imrBJy9qRUNSv1FlHCmDNtPGa3bLG8wS5p67Q8llPay+nG7fR6s+DRUlWyzrBVC4eKu3MRCrJ0mEw82qUxZXPzrLrybntst/7+QTFSbT4IKWUomsiIQiYsAG9GlnbeSFqU4pKptRg7dqy+v2b2NYh+4Fq8fgyAZQWTMzFD9ETWlm8WbrJIiyzt5bYezK6GKGbzOrvOMLPDeRAYxZ9Wg5VS1YxraXd93ETUUqpKSwBCRgAUTaTiiEYUqZeQdnO0igh5SYk43Uj/suPXGDv2In079ukfouqU8a7WLwRQJYCkSVdpAs4cgZjdssU2vZSLGLRLP8mOK7Mw0JAJLb9u5m5witrYXR+3heh0+yak8qFoImWJXTRkxeXT8fm2TsdYjtVNzm1KRFr/lE4h/tAnkDrSAwA4ZcZFqP3Xpe5OCkBECQHIrrGprVawfMF0y7U5dZ3lKgZlYsBv3Y2sLivsNFMlTzidh5euPFoCEFLZUDSRssNNNCQcFkhbtMGbcTup3nxDtbqRHn2lC6+vvUPfnvixBzBqwllSkWBGCQmMUcKWtVfVo6qkYsRNe3quYtBpNp0XZNfCzTVywu/oEbvzcDNCRoOWAIRUNrQcIGWHk1ty6+a9lr5BVtRI3K01ZJPq50wbr7eEq6qK1392ly6YRsf+AXW3b8SoCWcBGC7etiMWjaD1qpnSWWx24i7I9nTZTV8FAmuvl1kh2FkkuEH2WRnX7McuoKkhluU+ft2FdbQEIGQEwkgTKTucoiFeUiSyjJCxxdyMZja5auEMrPzJ0+hcc6P+2ISP/CciU2ZmPF8rOJZ1sQlA787zWrQNuEuTuY3ANM+biuZ1uy1FZ1Dt9blaC8hwSi3mMs/NKhLVOGUcLQEIGWFQNJGywykd5aUGxSqyY9cZp3GwL4FfPfRVdD70EABgwoQJ+PbGHfjSxj2WYsBtF5sbQSETQGZPoaVtna6668ykbKJ0QRQ758uDyElMe6nXcgMtAQgZeVA0kbLDSVhYPe5lHIpTZ9zg24cQ//YNeOjE9iOPPIJrr70WAFClKLoYiFYrUNVhA0ezeDGvG3AWFE7REqvH127vtvQWshILKzZ0ORbQB1HsnA/B4SSmOc+NEJIrFE2k7HASFlaPuxEsGnY30cPbH0ff//xI3z5y5AhOPvnkjLXJxMv6XXF98K5dhMVOUDhFS2SjYKywOk+ZVYORUi12dhLTnOdGCMkViiZSljhFKnKpQbG6uaaPDeDA/Vfr26tXr8btt98ufX+ZuNm6p0fqLu5mbUHWc/kRC6Vc7OwkpvNVS0UIGTlQNBFiwnxzfbtrK/6+6V798YMHD2LixIm2x5CJl3hfIsuVHEBWVGppWyeWtHVmOWs7RUui1YrjuBhALhZqbV5v5/JdKtiJabe1VH5tCwghlQ9FExkReOmc0rZXP/knPPufVyF99C0AwMkN/4oZV9+GHa+l0WSvmaTiRiDblXx0VUiaUjOv0ylaIrM6iighjBs72lEILF8wPat7TgkLtF45syKEg13BvEzAcjwKIUSDoomUHF5+6d/V/jwe3XEAKVVFWAhcM2sy7m6akfU8r51TJx/ag+1fulTfnvSJB6GcOtn1TdRtMXoimXI0TTSu0ylaclhSk3Q0mbZMCxrRrnsypWbNZ6s0wSAT0WOUbAHL8SiEEA2KJlJSeIkI3dX+PB7Z3q1vp1RV3zYLJ7edU6qq4n3vex927NgBAIi+qxGnXLEcwmDo5OYmaiVucpmtZlynXQrKb7Gz+bprhpyVKJgAuYiWCVh22BFCADqCkyJg58rs5PZt5NEdByyPb7VfJhqM+3fv3o1QKKQLpt///veILlyRIZg03NxEmxpi2LZsLl5quQzbls2VOl7XViuOjuFui7b9uoN7ue6VgFcRxA47QgjASBMpMHaRJG3bCq2A2hj5sJthZkzx1UQUJFPZ7kNGMXHddddh7dq1AICzzz4bf/nLXxAOhzHpd1sCa1OX1SMtXzAdwLAbuDmN56XDy69x5EjzMJJF5KIRBccG0+ywI4RYQtFECoosorFyYxeOJu1tFc2pOtkgXCEyi3mtvIeiEQUrLp+O95yayogkrV+/HgsXLtS3g2xTd+svlWv3llOxs1ubBW1/JSL7XFdcPixg2T1HCDEj1AAmi9vR2Nio7ty5M6/vQcqHs5Y9KTVbdEssGsG2ZXOzapo0qpUQBhwEWG21gvnJ3+Huu+/W9/X396O6ujrrueXagm41DiaihLFq4QzLNnu3z60USv1zLfX1EVKpCCF2qaraaPkYRRPxSi5/zGe3WKe7vCAAvNRyGQDr7jmrsSFGUkffxivf+Ii+HfvQZzD+wg+jbyCZ95tTIW+EsmtdW62gelRV1hp4ky4dRqKIJaRUoGgigZHrH3PZ60dXhVyN8ACGI00y7ITZW52/wqHN39K3z7h1LcLVNRnPiShhV+NOvFLoG6HbqJ7bNVBUFQ7Zd9jpu08IyR070cSaJuKJXCfFy+p6AGQJCiUsABVIpodv/eZ6IqsbuVW9ijp4HN33Xw2kBgEAp1ywELVzbrRcYyKZykj7BWVwmOu184pbmwPzGqyuKUDTx0Iy0grzCSkXKJqIJ4L4Y+40kNZ8s5ZFN2SdeKsWzsCqhTP0brSBfc+hZ91K/T0m3fw9KNHTXa8XsBY3XiMvhb4RWolHGdoaZNfUyrWcpo/5Y6QV5hNSLlA0EU/k84+5TEzJOsEGjg9Kb+Tbls3F5TMn4rzzzsP+ri4AQPW734/6j30V/ccGXacCjRjFjRcTTg2v1y6ILjogU3TKzl1bQ6FMH5nqs4fDhQkpTSiaiCcK8cdclh4yzkSzSzsd7Evg2WefxaxZs/R9zz33HBobG/Xju43AGDGKGz+pNi/Xzo8os8IsRGV1VdoaCmH6GNS5VTJ+/bYIIfmFool4It9/zGU3VAE1Y4isHa8/cTdmrd4OAFDGn4nGJd/FK+GJ0Kr6zOdQE1EgBNA7II8+mcWNn1Sb22vX3hHHbY/tzvKgshNlbiM3TmuQRcNqqxUcTQZj+ljo2q5yxS6NTQgpDhRNxDP5/GMuu6G6Idn7Kg4+/El9e8KVKxA5pxEHjxzLimRYncOZy56UHtvcXVYTUSzTXDURxXaNTtdOE40yt3OzKGvviGPlxq4MwecUubFbgxvX8lzFMoucCSHlCkUTKSn83jgPbfke3nquHQAglNGY/O8/g6gaFjBuIhm11YpltKm2Wsl6ncU4Otv9VhijQ9FqBapq7V5uxJgOs0sz+o3cuHUtzwUWORNCyhWKJlJSuG2T10gNHMYrD1yrb4+79N9x8sxLLJ+rza+TRUpklmVW+/skqbzegSTaO+JScaEJJfOMObvUoIY5HWYVlTPiV4DmOy3EImdCSLlC0URKCi9t8kd2/gK9v/muvj35cz9DaMxJ0ucLDBeQW6WwDkuiPFb77cSdLDVmjgx5sZUNC5GVInQSRX4jN/nubGORMyGkXKFoIlkUsx3ceEOViZJ08igO3Helvl0z+xpEP3Ct5XM1jFEdDXMKy0vayE7cyVJjTpEhGTLHbjvh5jdyU6jONhY5E0LKkVCxF0BKC+2mGe9LQMXwTbO9I16wNTQ1xLBt2VzELMRK/95tGYLpu089i+nzPwGBodqjaESBABCNKKitHvrvWDQijeoYozXN86YiooQzHpeJj6aGGFYtnCE9B6sokJ90WSwakY44sVovMHTufkez2HW2EULISIeRJpJBodvB7aJaxmiOmk4h/tAnkDrSAwC44YYb8KMf/QgA8IkPOb+PbJaXMYrkNW3U1BCTRsSsolNe6rXczIPLR5qLnW2EECKHoolkUMibplMqqKkhhp37D+GH636Fg2tv11+3e/dunHfeeZ7ey23xsde0kZeiZqvnamnD2hPdc4cTyQxDT7vC9XzAzjZCCJFD0UQyKORN0ymq9fM/vII1S6/HwMu7AQCjY+fizP/zdbyYOhXeJFP+io+9HFcTgY/uOICUqiIsBK6ZNRl3N2Wn+dzUFuWj/oidbYQQIoeiiWRQyJumXVTrhRdewMLzz9X3TfjIfyIyZSaODqb1+hq3AsicAlyzuL4oRc3tHXGs3xXXjStTqor1u+JonDLOVdG4OU0aRCrVKj2qDTtmZxshhGRC0UQyKGQ7uCyqldj6f3Hu6k0AgFB1FGd85v9BhIe/qlpExU2EpVTmnHkdjeImTZprKlV2bVYtnIFty+ZaPp9iihAykqFoIlkUqh3cHNUafOvviH/n3/TH33X1nTh+1uys14WFcB1hKYU5Z06jUTTTTTcz4Ixp0lxTqV6uTaHEJ4UZIaSUoeUAKRpa234sGsHh/30sQzAdOXIE9yz7rKUFgNu5bLJ9dvvzgRt/JrO1gxv7Ay8WCVZ4uTaFsCLIp91Fe0ccs1u24KxlT2J2y5aCWmgQQioHiiZSVC565yl45s6L0PfbHwMAVq9eDVVVcfLJJ2eIKs1vSdu2wjyXbXbLFqk/U5CF7U43ZLcCzShCZOdujLq4eY4dsmtgtb8Q4jNfwqwUvMcIIZUB03OkaDzyyCO4/vrr9e1XX30Vp59+esZzZKlCu2J1u0G22nPnTBtv287vNk3kJm3lxZ/JKELcpElzSaV6KfovRFdlvoRZKaRoCSGVASNNpOAcP34cp556qi6YPv3pT0NV1SzBJMMpwmKXDotFI1h0fgzrd8UtIw/tHXHUr3waS9o6XUUm3ERHrNJoQnJuhfRD8hKpyjUV6AYvkS8vlEKKlhBSGTDSRArKr3/9a1x88cX69gsvvIBp06Z5Po5dhMUuqrNt2VzMbtliKXRWbuzC0WTa0zw5Nzdkq47EOdPGY/2ueNH9kNxGqgrRVZkvuwsadhJCgoKiiRQEVVVx4YUX4tlnnwUAzJs3D7/85S8hhCzmYk8uXVYyodM7kPT8Orc3ZCtx0jhlXFl1iuW7qzJfwoyGnYSQoKBoInln9+7dqK+v17e3bduG97///b6Pl2v7u5caI/PrzORyQy6UtQNQPq38+bgmhfQeI4RUNhRNJK9cd911WLt2LQDg7LPPxl/+8heEw2GHV9njVNgbFsLSliB8IqolEzqjq0LoS1hHm2RCyHxDrokoEAJY2taJFRu6IATQN5As6o26VAw+i0khBSohpHJhITjJC93d3RBC6IJp/fr12LdvX86CCXCuI7pm1mTLx7X9sgLoFZdPzyp2BoaG6dq18jc1xLBt2VysWVyPY4Np9A4koQLoSyT1/y5mm3shPJYIIWQkwEgTCZwvfelLuPvuu/Xt/v5+VFdXB3Z8WXotWq3oNgLVSgiJwTRUFZaDce0iD3ZpHLs0l5OJZSKZwpK2TrRu3lvQqFMu3WPlktYjhJBCQNFEAqO3txfjxo3Ttx944AHccsstgb+PVXpNCQu8fXRQL+YeSKYRUcKezB4BezHllOZy28Je6PSY3+4xpvUIISQTpudIIDz88MMZgqmnpycvggmwTq+NHVWFZDp7GO6Sts7AxmY4pbm8tLAXMj3m12OJaT1CCMmEkSaSE0ePHkVNTQ2OHz8OAPjCF76A1tbWvL+vOSJ05rInpc8NKkLilOayioD5OV7Q+O0eoykkIYRkQtFEfLNp0yYsWLBA3963bx/OPvvsoqxF1jGnEcTYDKc0l6yTTub/ZKzByne9kJ/uMZpCEkJIJhRNxDPpdBozZszAn//8ZwDAokWLsG7duqKuyU4waeQaIXHjyWQlTqxm4ZlrsEqxXoimkIQQkglFE/HEs88+i1mzZunbzz33HBobG4u4oiFiLgwrayK5RXb8prmsXtd/bDDLE6rUhsjSFJIQQjIRqotf6PqThagB8DMMia23ASxWVfW43WsaGxvVnTt35rRIUho0NTXhF7/4BQDgvPPOQ0dHB0Kh0uglsIrmGFFCAhBAMjX8fffTXedmHW5ExlnLnoTVvzwB4KWWywJbDyGEEG8IIXapqmoZDfB6x7sWwH2qql4M4DUAl+a6OFL6/O1vf4MQQhdMTz31FHbv3l1wwdTeEcfsli04a9mTWR1x5o662moF0Yiid9edNKYqQzABwXeCacIt3pdwNLSU1QWxXogQQkoXT+k5VVW/Y9gcD+CNYJdDSo3Pf/7zWLNmDQCguroahw4dwujRowu+DjeeQXbFzmdJuuuC7ARzGu9ihPVChBBSftiKJiHEQwCMf8W3qKr6FSHE+wDUqqq6XfK6mwDcBAB1dXVBrZUUkJ6eHkyYMEHf/v73v48bb7wx8Pdxm87yIkisKEQnmJcWfdYLEUJI+WErmlRVvdm8TwgxDsADABbZvO5hAA8DQzVNOa6RFJhvfOMbWLJkib7d29uLaDQa+Pu0d8TR/Phu3ZQy3pdA8+O7AWR3kOXqGVSIyI5XYcYhsoQQUl54KkoRQowC8BiAO1VV3Z+fJZFi0d/fDyGELpiWL18OVVXzIpgAYMWGriwX72RaxYoNXVnPzbUGSDakN0jR4td5mxBCSHng1XLg4wDOB/BFIcQXATyoqmpb8MsihWbdunW46qqr9O3u7m5Mnjw5r+9pbrm32x9EpCjfkR2m3AghpLLxWgj+IIAH87QWUgQGBwdxzjnnoLu7GwBwww034Ec/+hGA0ppwXy6ChCk3QgipXGhuOYL5/e9/jw9+8IP69h//+EfMmDEDgHW32tK2Tixp60QsIMFSW61YjhiprVYsn09BQgghpJiUhjMhKSiqquKiiy7SBdPs2bP10SgaVt1qWvWRnf+QF5YvmA4lLDL2KWGB5Qum53RcQgghJB9QNI0wXnjhBYRCIWzZsgUA8Jvf/Aa///3vIUSmeHHqSgvCGLKpIYbWK2dmFGe3XjmT0SRCCCElCdNzI4hPfepTeOihhwAA73jHO3DgwAEoinUqTNY+byQIY0im3AghhJQLjDSNAA4ePAghhC6YfvrTn+K1116TCibAun3eTKFHftiNUSGEEELyDSNNFc6qVavwH//xH/r2kSNHcPLJJzu+ztitFu9LQAAZA2YL5T+kdfCZ12A1RqXUKKXuQ0IIIbkjVDW/ht2NjY3qzp078/oeJJsjR46gpqZG3169ejVuv/1238crhgAwd/BZEYtGsG3Z3Lyuww9Wa48o4cANNQkhhASLEGKXqqqNlo9RNFUeP/nJT3DDDTfo26+++ipOP/30Iq7IH7NbtjjWVQEIzAIhSGRrL1WRRwghZAg70cSapgri+PHjOPXUU3XB9JnPfAaqqpalYALcF5oHZYEQJLnOyiOEEFJ6sKapQvj1r3+Niy++WN/es2cPpk4t75lnbjr4NDQLBC3aFGQ60c+xvA7vZf0TIYSUPow0lTmqqmLWrFm6YJo3bx7S6XTZCybAuoNPSJ4LDEdxtHqieF8CKnKLRPk9lpfhvUGulxBCSP6gaCpjOjs7EQqF8OyzzwIAtm3bhl/96ldZRpXlSlNDDKsWzsgwv1yzuB4xSbRGi+JYuZn7NeOUHWtJW6et7YHV2mVF4EGulxBCSP5geq5Mufbaa/HTn/4UAHDOOedg7969CIftfZXKEZn5pVVnmhbFCbKeyO41TrYHbo07Wf9ECCHlASNNZUZ3dzeEELpgeuKJJ/C3v/2tIgWTDKcojqxuyI8Zp9NrgogIBbleQggh+YOiqYz40pe+hClTpujb/f39uOKKK4q4ouLR1BDDtmVz8VLLZdi2bG5GRMdLPZETbpzRc40IBbleQggh+YPpuTKgt7cX48aN07cfeOAB3HLLLUVcUWljdDPPtRvN7IxuRa4RoSDXSwghJH/Q3LLEefjhh3HzzTfr2z09PTjttNMCf59KbnkP6tzo8k0IIZWPnbklI00lytGjR1FTU4Pjx48DAJqbm3HPPffk5b3MYqAc5rq5JchzY0SIEEJGNhRNJcimTZuwYMECfXvfvn04++yz8/Z+di3v5S4Igj43tx1xZio5kkcIISMFiqYSIp1OY8aMGfjzn/8MAFi0aBHWrVuX9/et5Jb3IM4tV8FTyZE8QggZSbB7rkR49tlnEQ6HdcH03HPPFUQwAZXd8p7ruQXh1k3zSkIIqQwomkqApqYmzJo1CwBQX1+PVCqFxkbLGrS8UMkt77meWxCCp5IjeYQQMpJgeq6I/O1vf8O73vUuffupp57Chz70oYKvo5ILnHM9tyAEj9fhvYQQQkoTiqYisXTpUtx///0AgOrqahw6dAijR48u2nr8FjiXA7mcWxCCp3neVNuxL4QQQsoDpucKTE9PD4QQumD6/ve/j/7+/qIKJiIniNSll+G9hBBCShdGmgrIN77xDSxZskTf7u3tRTQaLdp6iDNBpS4rOZJH8gNtKggpPSiaCkB/fz9OOukkfXv58uVYsWJF8RZEPEHBQwoNbSoIKU2Ynssz69atyxBM3d3dFEyEEFtoU0FIaULRlCcGBwdRV1eHq666CgBwww03QFVVTJ48ucgrI4SUOrSpIKQ0YXouD/zud7/DP/3TP+nbf/zjHzFjxgzp81m7QAgxQpsKQkoTRpoCRFVVXHTRRbpg+sAHPqCPRpERhOM0IaSyqGTDWULKGYqmgHjhhRcQCoWwZcsWAMCWLVvwu9/9DkII29exdoEQYoY2FYSUJkzPBcBNN92E7373uwCAd7zjHThw4AAURXH1WtYuEEKsYNcmIaUHI005cPDgQQghdMH005/+FK+99pprwQRU9rBcQgghpJKgaPLJqlWrEIsN/wo8cuQIrrnmGs/HYe0CIYQQUh4wPeeRI0eOoKamRt++55570Nzc7Pt4lTwslxBCCKkkKJo88JOf/AQ33HCDvv3qq6/i9NNPz/m4rF0ghBBCSh+m51xw/Phx1NbW6oLpM5/5DFRVDUQwEUIIIaQ8YKTJgV//+te4+OKL9e09e/Zg6lTWG41UaERKCCEjF0aaJKiqilmzZumC6dJLL0U6naZgGsHQiJQQQkY2FE0WdHZ2IhQK4dlnnwUAbNu2Db/85S8djSpJZUMjUkIIGdlQNJm49tpr0dDQAAA455xzMDg4iPe///1FXhUpBWhESgghIxvWNJ2gu7sbU6ZM0befeOIJXHHFFUVcESk1OESVEEJGNow0AfjSl76UIZj6+/spmEgWNCIlhJCRzYiONPX29mLcuHH69re+9S189rOfLeKKSClDI1JCCBnZjFjR9PDDD+Pmm2/Wt3t6enDaaacVcUWkHKARKSGEjFxGXHru6NGjUBRFF0y33347VFWlYCKEEEKILSMq0rRp0yYsWLBA337xxRdx1llnFXFFhBBCCCkXRkSkKZ1OY/r06bpgWrRoEVRVpWAihBBCiGsqPtL07LPPYtasWfr2c889h8bGxiKuKDc4xoMQQggpDhUdafrwhz+sC6b6+nqkUqmyF0wc40EIIYQUh4qMNP3tb3/Du971Ln37qaeewoc+9KGirSeo6JDdGA9GmwghhJD8UnGRpqVLl+qCaezYsTh69GjRBVNQ0SGO8SCEEEKKR8WIpp6eHgghcP/99wMAvv/97+Ptt9/G6NGji7quIIe8ysZ1cIwHIYQQkn8qQjTdf//9mDBhgr7d29uLG2+8sYgrGibI6BDHeBBCCCHFo+xFU19fH5YuXQoAWLFiBVRVRTQaLe6iDAQZHWpqiGHVwhmIRSMQAGLRCFYtnMF6JkIIIaQAlH0heDQaxRNPPIH3vve9OOOMM4q9nCya503FnU88n5GiM0eHvBSKc4wHIYQQUhzKXjQBwBVXXFHsJUhxGvKqFYprokorFDe+lhBCCCHFpyJEU6ljFx2ijQAhhBBSHpR9TVO5QxsBQgghpDygaCoytBEghBBCygOKpiJDGwFCCCGkPGBNU5FxKhQnhBBCSGlA0VQC0EaAEEIIKX2YniOEEEIIcQFFEyGEEEKICyiaCCGEEEJcQNFECCGEEOICz6JJCDFOCHGxEOK0fCyIEEIIIaQU8SSahBATATwJ4AIAW4UQ4/OyKkIIIYSQEsOr5cB0AEtVVd0uhKgF8B4Am4NfFiGEEEJIaeEp0qSq6q9PCKZ/wlC06X/zsyxCCCGEkNLCNtIkhHgIgHGexxYAXwWwGEASQEryupsA3AQAdXV1gSyUEEIIIaSYCFVV/b1QiK8C+JOqqm12z2tsbFR37tzp6z0IIYQQQgqJEGKXqqqNVo95LQS/Qwhxw4nNKIC+3JZGCCGEEFIeeLUceBjA9UKI3wIIA3g6+CURQgghhJQenrrnVFXtBXBxntZCCCGEEFKyeLUcIC5p74ijdfNeHOxLYFI0guZ5U9HUECv2sogD/NwIIYTIoGjKA+0dcdz5xPNIJIeaC+N9Cdz5xPMAwBtwCcPPjRBCiB1lP3uuvSOO2S1bcNayJzG7ZQvaO+LFXhJaN+/Vb7waiWQKrZv3FmlFxA383AghhNhR1pGmUo0MHOxLeNpPSgN+boQQQuwo60hTqUYGJkUjnvaT0oCfGyGEEDvKWjSVamSged5URJRwxr6IEkbzvKmSV5BSgJ8bIYQQO8o6PTcpGkHcQiAVOzKgpQbZhVVe8HMjhBBih+8xKm7J5xgVc00TMBQZWLVwBm90hBBCCPGM3RiVso40MTJACCGEkEJR1qIJGBJOFEmEEEIIyTdlXQhOCCGEEFIoKJoIIYQQQlxA0UQIIYQQ4gKKJkIIIYQQF1A0EUIIIYS4gKKJEEIIIcQFFE2EEEIIIS6gaCKEEEIIcQFFEyGEEEKICyiaCCGEEEJcQNFECCGEEOICiiZCCCGEEBdQNBFCCCGEuICiiRBCCCHEBRRNhBBCCCEuEKqq5vcNhOgBsD+vb1L5nAbgzWIvosLgNc0PvK7Bw2uaH3hd80MlXNcpqqqOt3og76KJ5I4QYqeqqo3FXkclwWuaH3hdg4fXND/wuuaHSr+uTM8RQgghhLiAookQQgghxAUUTeXBw8VeQAXCa5ofeF2Dh9c0P/C65oeKvq6saSKEEEIIcQEjTWWAEGKcEOJiIcRpxV4LIYQQMlKhaCpxhBATATwJ4AIAW4UQlm2QxD1CiBohxC+FEP8lhPi5EGJUsddUKQgh3iGE+F2x10GIHfyeBs9I+btK0VT6TAewVFXV/wSwGcB7iryeSuBaAPepqnoxgNcAXFrk9VQEQohaAD8CMLbYa6kUhBDfF0I8I4S4q9hrqRT4Pc0bI+LvKkVTiaOq6q9VVd0uhPgnDEWb/rfYayp3VFX9jqqq/3ViczyAN4q5ngoiBWAxgCPFXkglIIRYCCCsqur7AUwSQryr2GuqEPg9zQMj5e9qVbEXQDIRQjwEYKph1xYAX8XQP/Ikhv7BEw9YXVNVVb8ihHgfgFpVVbcXaWlljc11LdaSKo1/AfDYif/eAuADAP5atNVUCKqqHgEAfk/zQ6X/XaVoKjFUVb1Z8tBnhRBfBTAfQFsBl1T2WF1TIcQ4AA8AWFT4FVUGNt9VEgxjAcRP/PcRAO8s4loIcWQk/F1leq7EEULcIYS44cRmFEBf8VZTGZwoUHwMwJ2qqnIuIilV3gYQOfHfJ4F/r0kJM1L+rvIfYenzMIDrhRC/BRAG8HSR11MJfBzA+QC+KIT4byHE4mIviBALdmEoJQcAMwG8XLylEOLIiPi7SnNLQggpQYQQpwD4HYDfAPgQgAtVVT1c3FURMrKhaCKEkBLlRHv8xQB+q6rqa8VeDyEjHYomQgghhBAXsKaJEEIIIcQFFE2EEEIIIS6gaCKEEEIIcQFFEyGEEEKICyiaCCGEEEJc8P8D9Ci820dMtOIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用散点图观测（10分）\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(Y_test,Y_predict)\n",
    "\n",
    "#the trendline    \n",
    "z = np.polyfit(Y_test,Y_predict,1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(Y_test,p(Y_test),color='black',ls='-')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "657f9fc5",
   "metadata": {},
   "source": [
    "''' 图中我们可以看到，如果完全拟合，散点应该和直线相重合，这里发现，y_test有一些的异常值， 而线性回归模型的一大缺点就是对异常值很敏感，会极大影响模型的准确性，因此，下一步，我们就根据这一点，对模型进行优化 '''"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bec4a2ba",
   "metadata": {},
   "source": [
    "**绘制残差图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 800,
   "id": "8ba4cb47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘制残差图查看模型的拟合情况（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 751,
   "id": "2fea1e60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Next_Tmax'>"
      ]
     },
     "execution_count": 751,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Y_observed')"
      ]
     },
     "execution_count": 751,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y_predicted')"
      ]
     },
     "execution_count": 751,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(Y_test, Y_predict)\n",
    "plt.xlabel('Y_observed')\n",
    "plt.ylabel('Y_predicted')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829bb892",
   "metadata": {},
   "source": [
    "**绘制相关性热图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "id": "824ad67a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Max.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 753,
   "id": "9a1bd047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = Max_scal.corr()\n",
    "mask = np.zeros_like(corr)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "with sns.axes_style(\"white\"):\n",
    "    f, ax = plt.subplots(figsize=(12,12))\n",
    "    ax = sns.heatmap(corr, mask=mask, annot=True,square=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23fb7a60",
   "metadata": {},
   "source": [
    "# 模型调优"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed225bd",
   "metadata": {},
   "source": [
    "**删除异常值**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 754,
   "id": "e4d54093",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 754,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#####================寻找异常值====================######\n",
    "\n",
    "Max_scal['Next_Tmax'].plot.box() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 755,
   "id": "d8e5f0b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.8575878207889565"
      ]
     },
     "execution_count": 755,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除掉Next_Tmax中小于下边缘值的记录\n",
    "low_whisker = -3.8575878207889565\n",
    "low_whisker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 756,
   "id": "d545e5d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6175</th>\n",
       "      <td>-2.695272</td>\n",
       "      <td>1.457979</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>4.911091</td>\n",
       "      <td>-0.210420</td>\n",
       "      <td>1.489658</td>\n",
       "      <td>2.281600</td>\n",
       "      <td>2.656311</td>\n",
       "      <td>2.268567</td>\n",
       "      <td>-0.017489</td>\n",
       "      <td>1.340116</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.786106</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>-3.304127</td>\n",
       "      <td>-4.113443</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>-1.939757</td>\n",
       "      <td>-2.267499</td>\n",
       "      <td>-1.412018</td>\n",
       "      <td>-1.386643</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845455</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "6175     -2.695272     1.457979         -4.087857  4.911091 -0.210420   \n",
       "7750     -3.304127    -4.113443         -4.087857 -1.939757 -2.267499   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "6175   1.489658   2.281600   2.656311   2.268567   -0.017489    1.340116   \n",
       "7750  -1.412018  -1.386643  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "6175  1.189286 -0.005000  2.772243  1.115004        -1.786106  -4.123453  \n",
       "7750 -1.758184 -2.082302 -0.911963 -0.845455        -2.358212  -4.123453  "
      ]
     },
     "execution_count": 756,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max_scal[Max_scal['Next_Tmax']<low_whisker] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 757,
   "id": "b37d2b3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7752, 16), (7752,))"
      ]
     },
     "execution_count": 757,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape,Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 758,
   "id": "51c6ed0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6175, 7750], dtype=int64)"
      ]
     },
     "execution_count": 758,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_index=Max_scal[Max_scal['Next_Tmax']<low_whisker].index.values\n",
    "drop_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 759,
   "id": "8e141720",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除Y中小于箱型图下边缘的值\n",
    "X=X.drop(drop_index)\n",
    "Y=Y.drop(drop_index) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 760,
   "id": "e59448b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7750, 16), (7750,))"
      ]
     },
     "execution_count": 760,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape,Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "id": "db57f18f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 761,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.22952887192574342\n",
      "RMSE: 0.47909171556784763\n"
     ]
    }
   ],
   "source": [
    "# 重新划分数据集\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=1)\n",
    "# 对训练集进行训练\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, Y_train) \n",
    "# 对测试集进行预测\n",
    "Y_pred = lr.predict(X_test)\n",
    "MSE = mean_squared_error(Y_test, Y_pred)\n",
    "RMSE = np.sqrt(mean_squared_error(Y_test, Y_pred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 762,
   "id": "a5d800ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 上一次评估结果\n",
    "# MSE: 0.22952887192574342\n",
    "# RMSE: 0.4962357527761606"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b44ba58",
   "metadata": {},
   "source": [
    "''' 可以看到，该模型的RMSE由原来的0.496降低到了0.479，小幅度的提升了模型的准确率;去除异常值，提高准确度。 '''"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b589f05",
   "metadata": {},
   "source": [
    "**特征筛选**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 763,
   "id": "016b016f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7750 rows x 16 columns]"
      ]
     },
     "execution_count": 763,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 764,
   "id": "f3b78bd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 764,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征筛选，选出和样本标签之间存在显著相关性（p<0.05)的特征，从而过滤掉对模型预测贡献不大的特征\n",
    "from sklearn.feature_selection import f_regression\n",
    "\n",
    "F,p=f_regression(X,Y)  # 一个特征对应一个F值和p值\n",
    "k=F.shape[0] - (p>0.05).sum() # 去掉p值<=0.05的特征，k就是剩下的特征数量\n",
    "k "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 765,
   "id": "87ec8acb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7750, 14)"
      ]
     },
     "execution_count": 765,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.36132577,  0.38307796, -0.52488856, ...,  1.18928568,\n",
       "         2.77224286,  1.11500407],\n",
       "       [ 0.72108401,  0.31158619,  0.08089519, ...,  1.18928568,\n",
       "        -0.31515742, -0.54215762],\n",
       "       [ 0.61960809, -0.61498177,  0.16293647, ...,  0.65302103,\n",
       "        -0.52621832, -0.7231326 ],\n",
       "       ...,\n",
       "       [-2.18789227, -1.54818379, -0.88766178, ..., -0.41752209,\n",
       "        -0.82121274, -0.75509512],\n",
       "       [-2.22171758, -1.55534234, -0.57077984, ..., -0.41752209,\n",
       "        -0.77904331, -0.71933797],\n",
       "       [ 2.64912642,  1.62440928,  3.04456067, ...,  1.99169648,\n",
       "         2.77224286,  2.86143459]])"
      ]
     },
     "execution_count": 765,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# F检验筛选特征\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "X_f=SelectKBest(f_regression,k=k).fit_transform(X,Y) \n",
    "X_f.shape    # 过滤掉了两个特征\n",
    "X_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 766,
   "id": "51e16e76",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(X_f,Y,test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 767,
   "id": "683676cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(xtrain,ytrain) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 768,
   "id": "5a298e26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.745518217297051, 0.7594863753705541)"
      ]
     },
     "execution_count": 768,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试集上的评分（R²）\n",
    "lr.score(xtrain,ytrain),lr.score(xtest,ytest) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 769,
   "id": "0e55a36d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.23443534187388365\n",
      "RMSE: 0.4841852350845528\n"
     ]
    }
   ],
   "source": [
    "ypred = lr.predict(xtest) \n",
    "MSE = mean_squared_error(ytest, ypred)\n",
    "RMSE = np.sqrt(mean_squared_error(ytest, ypred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dad4446",
   "metadata": {},
   "source": [
    "**PCA降维**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2988f48a",
   "metadata": {},
   "source": [
    "主成分分析的目的是从现有的特征中重建新的特征，新的特征剔除了原有特征中的冗余信息，因此更有区分度。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76712493",
   "metadata": {},
   "source": [
    "主成分分析的结果是得到新的特征，而不是简单地舍弃原来的特征列表中的一些特征。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 770,
   "id": "0b179bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca=PCA(n_components=0.97).fit(X,Y) \n",
    "X_pca=pca.transform(X)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 771,
   "id": "dabd7823",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.216577</td>\n",
       "      <td>3.103277</td>\n",
       "      <td>-0.110833</td>\n",
       "      <td>0.165686</td>\n",
       "      <td>0.790641</td>\n",
       "      <td>-0.681205</td>\n",
       "      <td>-1.317347</td>\n",
       "      <td>-0.238028</td>\n",
       "      <td>-0.203376</td>\n",
       "      <td>0.603971</td>\n",
       "      <td>-0.337200</td>\n",
       "      <td>0.358007</td>\n",
       "      <td>-0.423250</td>\n",
       "      <td>1.099815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.090162</td>\n",
       "      <td>-0.107501</td>\n",
       "      <td>1.262260</td>\n",
       "      <td>0.415918</td>\n",
       "      <td>0.528656</td>\n",
       "      <td>-0.648804</td>\n",
       "      <td>-1.089290</td>\n",
       "      <td>-0.077244</td>\n",
       "      <td>-0.327796</td>\n",
       "      <td>0.455522</td>\n",
       "      <td>0.200883</td>\n",
       "      <td>-0.248300</td>\n",
       "      <td>-0.819938</td>\n",
       "      <td>0.253945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.170936</td>\n",
       "      <td>-0.890398</td>\n",
       "      <td>0.501055</td>\n",
       "      <td>0.423675</td>\n",
       "      <td>0.775087</td>\n",
       "      <td>-1.372693</td>\n",
       "      <td>-0.972450</td>\n",
       "      <td>0.227718</td>\n",
       "      <td>0.223689</td>\n",
       "      <td>0.190134</td>\n",
       "      <td>0.099512</td>\n",
       "      <td>-0.201590</td>\n",
       "      <td>-0.922498</td>\n",
       "      <td>0.184477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.834243</td>\n",
       "      <td>1.217238</td>\n",
       "      <td>1.663132</td>\n",
       "      <td>0.726037</td>\n",
       "      <td>0.587636</td>\n",
       "      <td>-0.459220</td>\n",
       "      <td>-0.996600</td>\n",
       "      <td>-0.280367</td>\n",
       "      <td>-0.815869</td>\n",
       "      <td>0.995221</td>\n",
       "      <td>0.419671</td>\n",
       "      <td>-0.486277</td>\n",
       "      <td>-0.841180</td>\n",
       "      <td>-0.672248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.214938</td>\n",
       "      <td>0.125820</td>\n",
       "      <td>1.699884</td>\n",
       "      <td>0.871620</td>\n",
       "      <td>0.306261</td>\n",
       "      <td>0.069075</td>\n",
       "      <td>-0.711520</td>\n",
       "      <td>-0.439632</td>\n",
       "      <td>-0.165456</td>\n",
       "      <td>-1.432835</td>\n",
       "      <td>-0.681152</td>\n",
       "      <td>-0.127686</td>\n",
       "      <td>-0.932863</td>\n",
       "      <td>0.190224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7745</th>\n",
       "      <td>-1.786437</td>\n",
       "      <td>0.231800</td>\n",
       "      <td>-0.034922</td>\n",
       "      <td>0.032411</td>\n",
       "      <td>-3.623162</td>\n",
       "      <td>-0.618894</td>\n",
       "      <td>0.170518</td>\n",
       "      <td>0.229131</td>\n",
       "      <td>1.534717</td>\n",
       "      <td>-1.291132</td>\n",
       "      <td>-0.853182</td>\n",
       "      <td>-0.036252</td>\n",
       "      <td>-0.137269</td>\n",
       "      <td>-0.045560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-1.871831</td>\n",
       "      <td>-0.153308</td>\n",
       "      <td>-1.028628</td>\n",
       "      <td>-0.810918</td>\n",
       "      <td>-3.635659</td>\n",
       "      <td>0.140778</td>\n",
       "      <td>-0.260850</td>\n",
       "      <td>0.833126</td>\n",
       "      <td>0.884765</td>\n",
       "      <td>0.511711</td>\n",
       "      <td>-0.727532</td>\n",
       "      <td>0.529375</td>\n",
       "      <td>-0.279357</td>\n",
       "      <td>-0.127452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-1.904300</td>\n",
       "      <td>-0.383570</td>\n",
       "      <td>-1.415379</td>\n",
       "      <td>-0.856730</td>\n",
       "      <td>-3.348666</td>\n",
       "      <td>-0.376563</td>\n",
       "      <td>-0.161274</td>\n",
       "      <td>0.946401</td>\n",
       "      <td>1.242176</td>\n",
       "      <td>0.431955</td>\n",
       "      <td>-0.515940</td>\n",
       "      <td>0.329307</td>\n",
       "      <td>-0.214308</td>\n",
       "      <td>-0.169483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-1.829787</td>\n",
       "      <td>-0.559760</td>\n",
       "      <td>-1.535874</td>\n",
       "      <td>-0.621879</td>\n",
       "      <td>-2.943078</td>\n",
       "      <td>-1.374214</td>\n",
       "      <td>-0.052400</td>\n",
       "      <td>1.204855</td>\n",
       "      <td>1.792964</td>\n",
       "      <td>0.294714</td>\n",
       "      <td>-0.109837</td>\n",
       "      <td>-0.169241</td>\n",
       "      <td>-0.115426</td>\n",
       "      <td>-0.188458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>8.725825</td>\n",
       "      <td>3.056087</td>\n",
       "      <td>4.609578</td>\n",
       "      <td>4.595430</td>\n",
       "      <td>8.330166</td>\n",
       "      <td>9.435757</td>\n",
       "      <td>5.962135</td>\n",
       "      <td>0.194339</td>\n",
       "      <td>10.953023</td>\n",
       "      <td>3.910947</td>\n",
       "      <td>0.153466</td>\n",
       "      <td>1.686319</td>\n",
       "      <td>-0.332968</td>\n",
       "      <td>0.823091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6   \\\n",
       "0    -0.216577  3.103277 -0.110833  0.165686  0.790641 -0.681205 -1.317347   \n",
       "1    -1.090162 -0.107501  1.262260  0.415918  0.528656 -0.648804 -1.089290   \n",
       "2    -1.170936 -0.890398  0.501055  0.423675  0.775087 -1.372693 -0.972450   \n",
       "3    -0.834243  1.217238  1.663132  0.726037  0.587636 -0.459220 -0.996600   \n",
       "4    -1.214938  0.125820  1.699884  0.871620  0.306261  0.069075 -0.711520   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "7745 -1.786437  0.231800 -0.034922  0.032411 -3.623162 -0.618894  0.170518   \n",
       "7746 -1.871831 -0.153308 -1.028628 -0.810918 -3.635659  0.140778 -0.260850   \n",
       "7747 -1.904300 -0.383570 -1.415379 -0.856730 -3.348666 -0.376563 -0.161274   \n",
       "7748 -1.829787 -0.559760 -1.535874 -0.621879 -2.943078 -1.374214 -0.052400   \n",
       "7749  8.725825  3.056087  4.609578  4.595430  8.330166  9.435757  5.962135   \n",
       "\n",
       "            7          8         9         10        11        12        13  \n",
       "0    -0.238028  -0.203376  0.603971 -0.337200  0.358007 -0.423250  1.099815  \n",
       "1    -0.077244  -0.327796  0.455522  0.200883 -0.248300 -0.819938  0.253945  \n",
       "2     0.227718   0.223689  0.190134  0.099512 -0.201590 -0.922498  0.184477  \n",
       "3    -0.280367  -0.815869  0.995221  0.419671 -0.486277 -0.841180 -0.672248  \n",
       "4    -0.439632  -0.165456 -1.432835 -0.681152 -0.127686 -0.932863  0.190224  \n",
       "...        ...        ...       ...       ...       ...       ...       ...  \n",
       "7745  0.229131   1.534717 -1.291132 -0.853182 -0.036252 -0.137269 -0.045560  \n",
       "7746  0.833126   0.884765  0.511711 -0.727532  0.529375 -0.279357 -0.127452  \n",
       "7747  0.946401   1.242176  0.431955 -0.515940  0.329307 -0.214308 -0.169483  \n",
       "7748  1.204855   1.792964  0.294714 -0.109837 -0.169241 -0.115426 -0.188458  \n",
       "7749  0.194339  10.953023  3.910947  0.153466  1.686319 -0.332968  0.823091  \n",
       "\n",
       "[7750 rows x 14 columns]"
      ]
     },
     "execution_count": 771,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca=pd.DataFrame(X_pca)\n",
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 772,
   "id": "1dfbbfdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7750, dtype: float64"
      ]
     },
     "execution_count": 772,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 773,
   "id": "ae907fd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.228159</td>\n",
       "      <td>0.259139</td>\n",
       "      <td>-0.363300</td>\n",
       "      <td>0.214156</td>\n",
       "      <td>-0.098169</td>\n",
       "      <td>0.389183</td>\n",
       "      <td>0.420652</td>\n",
       "      <td>0.400378</td>\n",
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       "      <td>0.192053</td>\n",
       "      <td>0.162411</td>\n",
       "      <td>0.025265</td>\n",
       "      <td>-0.008065</td>\n",
       "      <td>0.076506</td>\n",
       "      <td>0.069054</td>\n",
       "      <td>0.108524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.177762</td>\n",
       "      <td>0.259298</td>\n",
       "      <td>-0.115402</td>\n",
       "      <td>0.139626</td>\n",
       "      <td>0.253892</td>\n",
       "      <td>-0.080516</td>\n",
       "      <td>-0.148532</td>\n",
       "      <td>-0.188108</td>\n",
       "      <td>-0.188080</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>-0.060552</td>\n",
       "      <td>0.175085</td>\n",
       "      <td>0.061905</td>\n",
       "      <td>0.573748</td>\n",
       "      <td>0.580675</td>\n",
       "      <td>0.008013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.041856</td>\n",
       "      <td>0.375808</td>\n",
       "      <td>0.133046</td>\n",
       "      <td>-0.096357</td>\n",
       "      <td>0.346909</td>\n",
       "      <td>0.204690</td>\n",
       "      <td>0.055840</td>\n",
       "      <td>-0.139376</td>\n",
       "      <td>-0.192081</td>\n",
       "      <td>0.314158</td>\n",
       "      <td>-0.091826</td>\n",
       "      <td>0.459231</td>\n",
       "      <td>0.361806</td>\n",
       "      <td>-0.282789</td>\n",
       "      <td>-0.231722</td>\n",
       "      <td>0.160499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.128714</td>\n",
       "      <td>-0.109038</td>\n",
       "      <td>-0.004835</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.086365</td>\n",
       "      <td>-0.269335</td>\n",
       "      <td>-0.083357</td>\n",
       "      <td>0.229271</td>\n",
       "      <td>0.328601</td>\n",
       "      <td>-0.366989</td>\n",
       "      <td>0.459058</td>\n",
       "      <td>0.392905</td>\n",
       "      <td>0.459881</td>\n",
       "      <td>0.010425</td>\n",
       "      <td>0.048139</td>\n",
       "      <td>-0.099735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.521499</td>\n",
       "      <td>-0.132052</td>\n",
       "      <td>0.415585</td>\n",
       "      <td>0.200768</td>\n",
       "      <td>-0.109847</td>\n",
       "      <td>0.064168</td>\n",
       "      <td>0.077508</td>\n",
       "      <td>0.086216</td>\n",
       "      <td>0.099563</td>\n",
       "      <td>0.174102</td>\n",
       "      <td>0.008157</td>\n",
       "      <td>-0.070494</td>\n",
       "      <td>0.084456</td>\n",
       "      <td>0.188751</td>\n",
       "      <td>0.208160</td>\n",
       "      <td>0.580179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.234427</td>\n",
       "      <td>0.185108</td>\n",
       "      <td>-0.007308</td>\n",
       "      <td>0.228682</td>\n",
       "      <td>0.630832</td>\n",
       "      <td>-0.071018</td>\n",
       "      <td>-0.105731</td>\n",
       "      <td>-0.007234</td>\n",
       "      <td>0.102064</td>\n",
       "      <td>0.046327</td>\n",
       "      <td>0.409067</td>\n",
       "      <td>-0.213486</td>\n",
       "      <td>-0.445201</td>\n",
       "      <td>-0.111096</td>\n",
       "      <td>-0.084715</td>\n",
       "      <td>-0.031756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.377125</td>\n",
       "      <td>-0.085739</td>\n",
       "      <td>0.078499</td>\n",
       "      <td>0.002232</td>\n",
       "      <td>0.043436</td>\n",
       "      <td>0.071895</td>\n",
       "      <td>0.110212</td>\n",
       "      <td>0.108762</td>\n",
       "      <td>0.049060</td>\n",
       "      <td>0.444529</td>\n",
       "      <td>-0.093587</td>\n",
       "      <td>-0.110817</td>\n",
       "      <td>0.207857</td>\n",
       "      <td>0.087366</td>\n",
       "      <td>0.163401</td>\n",
       "      <td>-0.717383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.013714</td>\n",
       "      <td>-0.274986</td>\n",
       "      <td>-0.115457</td>\n",
       "      <td>0.835690</td>\n",
       "      <td>-0.034486</td>\n",
       "      <td>-0.002497</td>\n",
       "      <td>-0.048047</td>\n",
       "      <td>-0.071086</td>\n",
       "      <td>-0.092055</td>\n",
       "      <td>-0.053476</td>\n",
       "      <td>-0.223025</td>\n",
       "      <td>0.311199</td>\n",
       "      <td>-0.028485</td>\n",
       "      <td>-0.119224</td>\n",
       "      <td>-0.142026</td>\n",
       "      <td>-0.103987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.370291</td>\n",
       "      <td>-0.141150</td>\n",
       "      <td>0.121759</td>\n",
       "      <td>0.177218</td>\n",
       "      <td>-0.215333</td>\n",
       "      <td>-0.003833</td>\n",
       "      <td>-0.205263</td>\n",
       "      <td>-0.251031</td>\n",
       "      <td>-0.129045</td>\n",
       "      <td>0.434367</td>\n",
       "      <td>0.584006</td>\n",
       "      <td>-0.194281</td>\n",
       "      <td>0.238188</td>\n",
       "      <td>0.003956</td>\n",
       "      <td>-0.061626</td>\n",
       "      <td>0.022683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.042747</td>\n",
       "      <td>-0.017327</td>\n",
       "      <td>0.098680</td>\n",
       "      <td>-0.178946</td>\n",
       "      <td>-0.295307</td>\n",
       "      <td>-0.123886</td>\n",
       "      <td>-0.067586</td>\n",
       "      <td>0.010266</td>\n",
       "      <td>0.076458</td>\n",
       "      <td>0.310972</td>\n",
       "      <td>0.161651</td>\n",
       "      <td>0.616572</td>\n",
       "      <td>-0.574300</td>\n",
       "      <td>0.036589</td>\n",
       "      <td>0.062666</td>\n",
       "      <td>-0.070417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.228455</td>\n",
       "      <td>0.371271</td>\n",
       "      <td>0.185369</td>\n",
       "      <td>0.123466</td>\n",
       "      <td>-0.328695</td>\n",
       "      <td>0.234673</td>\n",
       "      <td>0.281510</td>\n",
       "      <td>-0.140353</td>\n",
       "      <td>-0.417383</td>\n",
       "      <td>-0.404841</td>\n",
       "      <td>0.328316</td>\n",
       "      <td>0.009849</td>\n",
       "      <td>-0.060252</td>\n",
       "      <td>-0.034421</td>\n",
       "      <td>0.054214</td>\n",
       "      <td>-0.211079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.034273</td>\n",
       "      <td>-0.637297</td>\n",
       "      <td>-0.063050</td>\n",
       "      <td>-0.208273</td>\n",
       "      <td>0.323071</td>\n",
       "      <td>0.465238</td>\n",
       "      <td>0.257615</td>\n",
       "      <td>-0.075044</td>\n",
       "      <td>-0.237589</td>\n",
       "      <td>-0.093466</td>\n",
       "      <td>0.184750</td>\n",
       "      <td>0.156335</td>\n",
       "      <td>-0.079597</td>\n",
       "      <td>0.142051</td>\n",
       "      <td>0.039583</td>\n",
       "      <td>0.025827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.352815</td>\n",
       "      <td>0.057000</td>\n",
       "      <td>0.671374</td>\n",
       "      <td>0.083665</td>\n",
       "      <td>0.102315</td>\n",
       "      <td>0.319565</td>\n",
       "      <td>-0.077398</td>\n",
       "      <td>-0.062434</td>\n",
       "      <td>0.416761</td>\n",
       "      <td>-0.181899</td>\n",
       "      <td>-0.123503</td>\n",
       "      <td>0.028882</td>\n",
       "      <td>-0.074246</td>\n",
       "      <td>0.140435</td>\n",
       "      <td>-0.075031</td>\n",
       "      <td>-0.195759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.048350</td>\n",
       "      <td>0.064477</td>\n",
       "      <td>0.013980</td>\n",
       "      <td>0.004915</td>\n",
       "      <td>0.015767</td>\n",
       "      <td>-0.187600</td>\n",
       "      <td>0.140001</td>\n",
       "      <td>0.123015</td>\n",
       "      <td>-0.138519</td>\n",
       "      <td>0.039964</td>\n",
       "      <td>0.012322</td>\n",
       "      <td>0.013607</td>\n",
       "      <td>0.012477</td>\n",
       "      <td>0.666108</td>\n",
       "      <td>-0.676660</td>\n",
       "      <td>-0.016238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6   \\\n",
       "0  -0.228159  0.259139 -0.363300  0.214156 -0.098169  0.389183  0.420652   \n",
       "1  -0.177762  0.259298 -0.115402  0.139626  0.253892 -0.080516 -0.148532   \n",
       "2   0.041856  0.375808  0.133046 -0.096357  0.346909  0.204690  0.055840   \n",
       "3   0.128714 -0.109038 -0.004835 -0.019345  0.086365 -0.269335 -0.083357   \n",
       "4   0.521499 -0.132052  0.415585  0.200768 -0.109847  0.064168  0.077508   \n",
       "5   0.234427  0.185108 -0.007308  0.228682  0.630832 -0.071018 -0.105731   \n",
       "6   0.377125 -0.085739  0.078499  0.002232  0.043436  0.071895  0.110212   \n",
       "7   0.013714 -0.274986 -0.115457  0.835690 -0.034486 -0.002497 -0.048047   \n",
       "8  -0.370291 -0.141150  0.121759  0.177218 -0.215333 -0.003833 -0.205263   \n",
       "9   0.042747 -0.017327  0.098680 -0.178946 -0.295307 -0.123886 -0.067586   \n",
       "10  0.228455  0.371271  0.185369  0.123466 -0.328695  0.234673  0.281510   \n",
       "11 -0.034273 -0.637297 -0.063050 -0.208273  0.323071  0.465238  0.257615   \n",
       "12 -0.352815  0.057000  0.671374  0.083665  0.102315  0.319565 -0.077398   \n",
       "13  0.048350  0.064477  0.013980  0.004915  0.015767 -0.187600  0.140001   \n",
       "\n",
       "          7         8         9         10        11        12        13  \\\n",
       "0   0.400378  0.343841  0.192053  0.162411  0.025265 -0.008065  0.076506   \n",
       "1  -0.188108 -0.188080  0.000994 -0.060552  0.175085  0.061905  0.573748   \n",
       "2  -0.139376 -0.192081  0.314158 -0.091826  0.459231  0.361806 -0.282789   \n",
       "3   0.229271  0.328601 -0.366989  0.459058  0.392905  0.459881  0.010425   \n",
       "4   0.086216  0.099563  0.174102  0.008157 -0.070494  0.084456  0.188751   \n",
       "5  -0.007234  0.102064  0.046327  0.409067 -0.213486 -0.445201 -0.111096   \n",
       "6   0.108762  0.049060  0.444529 -0.093587 -0.110817  0.207857  0.087366   \n",
       "7  -0.071086 -0.092055 -0.053476 -0.223025  0.311199 -0.028485 -0.119224   \n",
       "8  -0.251031 -0.129045  0.434367  0.584006 -0.194281  0.238188  0.003956   \n",
       "9   0.010266  0.076458  0.310972  0.161651  0.616572 -0.574300  0.036589   \n",
       "10 -0.140353 -0.417383 -0.404841  0.328316  0.009849 -0.060252 -0.034421   \n",
       "11 -0.075044 -0.237589 -0.093466  0.184750  0.156335 -0.079597  0.142051   \n",
       "12 -0.062434  0.416761 -0.181899 -0.123503  0.028882 -0.074246  0.140435   \n",
       "13  0.123015 -0.138519  0.039964  0.012322  0.013607  0.012477  0.666108   \n",
       "\n",
       "          14        15  \n",
       "0   0.069054  0.108524  \n",
       "1   0.580675  0.008013  \n",
       "2  -0.231722  0.160499  \n",
       "3   0.048139 -0.099735  \n",
       "4   0.208160  0.580179  \n",
       "5  -0.084715 -0.031756  \n",
       "6   0.163401 -0.717383  \n",
       "7  -0.142026 -0.103987  \n",
       "8  -0.061626  0.022683  \n",
       "9   0.062666 -0.070417  \n",
       "10  0.054214 -0.211079  \n",
       "11  0.039583  0.025827  \n",
       "12 -0.075031 -0.195759  \n",
       "13 -0.676660 -0.016238  "
      ]
     },
     "execution_count": 773,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "components=pca.components_   #14个新特征的特征向量(每一行) \n",
    "components=pd.DataFrame(components) \n",
    "components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 774,
   "id": "bfb8048c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25542858, 0.12862968, 0.08850255, 0.08191585, 0.07267401,\n",
       "       0.06801911, 0.05608564, 0.05187105, 0.05015169, 0.04070604,\n",
       "       0.0339251 , 0.02304823, 0.01785588, 0.01306852])"
      ]
     },
     "execution_count": 774,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_ #主成分的解释方差百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 775,
   "id": "b4d9dfb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 重新划分训练集和测试集\n",
    "Xtrain,Xtest,Ytrain,Ytest=train_test_split(X_pca,Y, test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 776,
   "id": "3f83fa12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(Xtrain,Ytrain) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 777,
   "id": "a4504682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7383072663535957, 0.7383072663535957)"
      ]
     },
     "execution_count": 777,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型在训练集和测试机上的评分（R²）\n",
    "lr.score(Xtrain,Ytrain),lr.score(Xtrain,Ytrain)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7873e6",
   "metadata": {},
   "source": [
    "# 随机森林回归 RandomForestRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 778,
   "id": "d44d5bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# 1.使用集成算法-随机森林回归器\n",
    "# 2.使用网格搜索寻找主要参数的最优取值组合\n",
    "# 3.使用去除异常值后的训练集数据训练模型（15分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 779,
   "id": "1ebc7d9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5425, 14)"
      ]
     },
     "execution_count": 779,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain.shape"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e3c92fa0",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 785,
   "id": "db4a2122",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid= {'n_estimators': range(1,50,10),'max_depth': range(1,10)},\n",
    "\n",
    "forest_reg= RandomForestRegressor()\n",
    "GS= GridSearchCV(forest_reg, param_grid, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 786,
   "id": "804fb020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
       "             param_grid=({'max_depth': range(1, 10),\n",
       "                          'n_estimators': range(1, 50, 10)},))"
      ]
     },
     "execution_count": 786,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.fit(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 787,
   "id": "40751673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 9, 'n_estimators': 41}"
      ]
     },
     "execution_count": 787,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看最优参数组合\n",
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 788,
   "id": "ca0fa607",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用上面得到的最优参数组合重新实例化模型\n",
    "rfr=RandomForestRegressor(n_estimators=181,\n",
    "                         max_depth=9,).fit(X_train,Y_train)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 790,
   "id": "050059f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9142343258025186, 0.8575339561035077)"
      ]
     },
     "execution_count": 790,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr.score(X_train,Y_train),rfr.score(X_test,Y_test) \n",
    "# 随机森林回归算法的score返回的是R²，并不是mse(尽管实例化时用的criterion='mse') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 798,
   "id": "221e5caa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.1388656287466089\n",
      "RMSE: 0.3726467881877005\n"
     ]
    }
   ],
   "source": [
    "\n",
    "MSE = mean_squared_error(Y_test, rfr.predict(X_test))\n",
    "RMSE = np.sqrt(mean_squared_error(Y_test, rfr.predict(X_test))) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE)  "
   ]
  }
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